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Sensors based wearble systems for monitoring of human movement & falls
 

Sensors based wearble systems for monitoring of human movement & falls

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    Sensors based wearble systems for monitoring of human movement & falls Sensors based wearble systems for monitoring of human movement & falls Document Transcript

    • 658 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012Sensors-Based Wearable Systems for Monitoring ofHuman Movement and FallsTal Shany, Stephen J. Redmond, Member, IEEE, Michael R. Narayanan, Member, IEEE, andNigel H. Lovell, Fellow, IEEEAbstract—The rapid aging of the world’s population, alongwith an increase in the prevalence of chronic illnesses andobesity, requires adaption and modification of current healthcaremodels. One such approach involves telehealth applications,many of which are based on sensor technologies for unobtrusivemonitoring. Recent technological advances, in particular, involvingmicroelectromechnical systems, have resulted in miniaturizedwearable devices that can be used for a range of applications.One of the leading areas for utilization of body-fixed sensors isthe monitoring of human movement. An overview of commonambulatory sensors is presented, followed by a summary ofthe developments in this field, with an emphasis on the clinicalapplications of falls detection, falls risk assessment, and energyexpenditure. The importance of these applications is considerablein light of the global demographic trends and the resultantrise in the occurrence of injurious falls and the decrease ofphysical activity. The potential of using such monitors in anunsupervised manner for community-dwelling individuals isimmense, but entails an array of challenges with regards to designconsiderations, implementation protocols, and signal analysisprocesses. Some limitations of the research to date and suggestionsfor future research are also discussed.Index Terms—Energy expenditure, falls detection, falls risk as-sessment, movement monitoring, wearable sensors.I. INTRODUCTIONGLOBAL demographic trends demonstrate a clear rise inthe proportion of elderly and chronically ill individuals.This is partly the result of improved healthcare systems,especially in developed nations. However, this trend ultimatelyposes considerable public health challenges, as most healthsystems are already stretched to their resource limits and do notpossess the capacity or framework to cater for the increasingneeds of an aging population [1].Concurrently, the occurrence of falls is also on the rise, withmore than a third of adults over 65 years of age said to fall atleast once per year. The consequences of falls, particularly ofthose which are followed by a “long lie” of an hour or more,are detrimental, both on a personal and a broader communitylevel, and include hospitalization, morbidity, and mortality[2]. This stresses the importance of monitoring individuals atrisk of falling, detecting falls, and implementing preventativeManuscript received February 24, 2011; accepted April 09, 2011. Date ofpublication April 21, 2011; date of current version February 03, 2012. The as-sociate editor coordinating the review of this paper and approving it for publi-cation was Prof. Aime Lay-Ekuakille.The authors are with the Graduate School of Biomedical Engineering,University of New South Wales, Sydney, NSW, 2052 Australia (e-mail:N.Lovell@unsw.edu.au).Digital Object Identifier 10.1109/JSEN.2011.2146246strategies to minimize falls risk by allocating suitable andtimely intervention when possible.Telehealth – the provision of health services involvinginformation, communications, measurement, and monitoringfrom a distance to patients’ homes – is forecast to become amainstream application. Its intended purpose is to alleviatethe expected pressure on healthcare systems and facilitate animproved level of care via long-term follow-up for diagnosticand intervention purposes [3].Telehealth applications are largely based on the use of sensortechnologies [4]. Console-based systems have been at the coreof telehealth for many years. Such systems typically includesensors to capture vital physiological measurements (heartrate, electrocardiogram, etc.) and are often equipped with auser viewing screen. In some instances, ubiquitous sensorsmay be placed around the home or a residential care facility,as in the “smart home” concept [5]. Remote observation ofactivity can then be conducted with various objectives in mind,including safety monitoring or to ensure proper care. Anotherapplication category involves body-fixed sensors that arereferred to as wearable sensors (or systems, since in many casesmore than one sensor is utilized). Regardless of the specificapplication, information collected via such telehealth sensors istransmitted over some distance and possibly further analyzed atits destination, before being viewed by predetermined recipientswhose role it is to monitor or act upon the data when required[3], [4].Technological advancements in the fields of electrical,mechanical and computer engineering, particularly involvingmicroelectromechnical systems (MEMS), have resulted insmaller and cheaper sensors that operate in a wireless manner.Most systems available commercially, or which are the subjectof current research, are enclosed in small cases that can beattached to the body using bands or belts [6]. In the future,an everyday object like a ring may be transformed into acompletely unobtrusive sensor for monitoring heart rate andblood oxygen saturation over long durations. Some sensors aresmall enough to be contained in a patch or a bandage; these maybe placed on the skin for collection of various measurements,like galvanic skin resistance or temperature. Another excitingresearch field involves the development of smart garments,where sensors are woven into real wearable objects, such asshirts and vests [7].The main area of utilization for wearable sensors ishealth-related. This can be divided into critical and noncriticalapplications [6]. The former would include monitoringindividuals whose condition might trigger an alarm when help is1530-437X/$26.00 © 2011 British Crown Copyright
    • SHANY et al.: SENSORS-BASED WEARABLE SYSTEMS FOR MONITORING OF HUMAN MOVEMENT AND FALLS 659required (e.g., an epileptic episode; an elderly person falling) orremotely tracking a casualty’s vital signs in combat situations.Noncritical use would encompass long-term monitoring ofpatients suffering from chronic conditions or of elderly peoplein their homes for purposes that are not necessarily alert-driven.For example, wearable sensors that quantify physical activitymay be used for patients with chronic lung disorders in orderto ensure their activity levels are sufficient, as inactivity tendsto exacerbate their condition. In relation to possible nonhealthapplications, wearable sensors used for monitoring physicalactivity can also be used by professional athletes to improve theefficiency of their training. Rescue operations may keep track ofan emergency crew’s location by using sensor-based wearablesystems including global positioning systems. Individualsexecuting tasks that require high levels of alertness, such astruck drivers or heavy machinery operators, may be monitoredto ensure their consciousness [6].It is therefore evident that the field of wearable sensorsfor telehealth is both broad and rapidly emerging. Thispaper focuses on sensor-based wearable systems in relationto ambulatory monitoring and presents an overview ofrecent developments in the field. Movement monitoringand classification are examined, along with a range of clinicalapplications of these ambulatory sensor technologies, withparticular emphasis on falls detection and falls risk assessment.II. AMBULATORY MONITORINGA. The Significance of Studying Human MotionHuman motion is a highly complex concept, which dependson and is, in turn, influenced by many factors, includingphysiological, anatomical, psychological, environmental, andsocial effects [6]. Movement reduction or modification canstem from various conditions, including stroke, osteoarthritis,and aging. Once mobility is impaired or reduced, a cycle ofdeterioration is commonly generated, whereby a person’scapacity to move is further diminished, mostly due to physicaland emotional reasons [8]. This highlights the need for properand timely interventions that address the specific issues whichhinder movement in each individual and provide the necessaryencouragement to keep frail and sick people as physically activeas possible. Clearly, to achieve these objectives, we must firstbe able to monitor and quantify movement, identify reducedor impaired movement, and estimate the value of administeredinterventions.Interestingly, though a real fall would never be an intentionalor positive event, in the context of ambulatory monitoring, a fallcan be regarded as a subcategory of human movement [9]. Itsoccurrence is related to the same factors that affect movement ingeneral and it often leads to reduced movement and an increasedrisk for subsequent fall episodes [8]. Monitoring and preventingfalls may therefore be possible using similar concepts to thoseapplied to movement monitoring.B. Assessment TechniquesThe study of human motion and falls employs manytechniques, including visual observations, video capture,interviews, diaries, questionnaires, physical measurements,and wearable ambulatory sensors. Self-report tools are simpleto administer, but capture partial information and suffer frominherent bias due to inaccurate recall, whether intentional ornot. Objective measurements use a variety of physical toolssuch as force plates, gait mats, and balance testing apparatus.Such tests are designed to be conducted in a clinical setting,usually in dedicated gait and falls clinics, and are relativelycostly and inappropriate for long-term monitoring of largepatient cohorts under real-life conditions [10].Miniature sensors or sensor systems that can be worn onthe body offer another means of gathering physical activityand falls data in a way that is suitable for clinical settingsbut has immense potential for long-term use, especially in thecommunity [11]. Additional advantages related to the use ofsuch wearable ambulatory monitors (WAMs) are: a) capturingobjective measurements of everyday or structured movements,including aspects that cannot be obtained by other assessmenttools and b) custom-tailored measurements can be developed toenable improved interventions and to quantify their effect overtime [12].C. Types of Wearable Ambulatory SensorsAccelerometers are used to measure acceleration along asensitive axis and over a particular range of frequencies. Sincethey measure acceleration due to gravity and movement, theactual component of movement-related acceleration needs to beseparated from the gravitational. The gravitational component isnevertheless useful in defining a subject’s postural orientation.There are several types of accelerometers available based onpiezoelectric, piezoresistive, or variable capacitance methods oftransduction. They all employ the same principle of operationof a mass that responds to acceleration by causing a spring oran equivalent component to stretch or compress proportionallyto the measured acceleration (Hooke’s law). Early availableaccelerometer sensor devices were of a uniaxial design;however, further advances in MEMS technology have lead tothe availability, at low-cost, of biaxial and triaxial devices, withtheir sensitive axes mounted orthogonally to one another.Vibrating gyroscopes measure angular velocity by takingadvantage of the Coriolis Effect. MEMS-based gyroscopes usea small vibrating mass within the sensor that undergoes a slightdisplacement when the gyroscope is rotated. If measured overtime, a change of angle in relation to an initial known anglecan be detected. These sensors have known limitations, whichinclude output drift over time, output offsets when the deviceis stationary, and a sensitivity which is limited to a particularrange of angular velocities.Magnetometers can be used to measure the orientation of abody segment in relation to the earth’s magnetic north, utilizingelectromagnetic induction. In order to work effectively, theorientation of the sensitive axis of the device must be alignedwith the magnetic field lines; composite devices containingmultiple devices on orthogonal axes are now used to compensatefor this requirement.Goniometers are fairly rudimentary devices, based on apotentiometric element which is attached to a joint’s rotationpoint to measure joint angle, although more advanced flexibleelectrogoniometers employ strain gauge elements. These
    • 660 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012sensors (along with inclinometers that are used to measure theslope of an object with respect to gravity using an artificialhorizon) are mainly employed in the determination of the rangeof motion of human body joints.Sole pressure sensors assess the pressure distribution acrossthe planter aspect of the foot by measuring the net groundreaction force. These are often realized using resistive orcapacitive-based strain gauges. Such pressure sensors havebeen incorporated into socks for increased ergonomics.Pedometers, also called step counters, detect human motionand, using specialized software, translate the measurementinto a count of the number of steps performed. In the past,pedometers were based on mechanical switches or pendulums,but nowadays they incorporate MEMS sensors, typicallyaccelerometers.Actometers are usually attached to an individual’s extremitiesin order to measure the magnitude of mechanically producedmovements. These sensors are basically a modified versionof the mechanism in a self-winding mechanical wrist watch,where the self-winding rotor responds to movement by drivingthe minute hand. The resulting output is a measurement of“actometer units” per known time period; this enables anestimation of total energy expenditure.In the context of clinical ambulatory monitoring, it seemsthat the most commonly used WAMs consist of accelerometers,gyroscopes, or both. Integrated systems that employaccelerometry with a gyroscope and a barometric pressuresensor (for measuring elevation) [13] or a magnetometer [14]have also been explored.D. Design and Usability ConsiderationsOn a technical level, the most important factors in designingan ambulatory sensing system are reliability (no randomvariance in measurements over time), durability, portability,continuous recording, high resolution at the desired frequencies,and an ability to filter the bandwidth as required. Wirelesscommunication is another fundamental feature; in some cases,real-time data processing via embedded intelligence is a must(e.g., in falls detection) [15].On a user interface level, the main considerations are size,weight, ease of use (preferably minimal or no intervention isrequired), number of sensors, and their location. Cost is clearly adeciding factor as well. Naturally, placing more sensors acrossthe body will generate more collectible data, but it is verylikely that compliance and usability will suffer. Conversely,especially in the context of unsupervised home monitoring,the goal is to allow data capture with minimal interference.While this is easier to achieve using ubiquitous home sensors,having multiple devices in several body locations is clearlycontradictory to this objective. The location of the sensor(s) onthe body greatly depends on the type of measurement desired.For example, the wrist may be an ideal location for monitoringtremors associated with Parkinson’s disease, but is consideredinadequate for studying patterns of locomotion [6]. Placing aWAM at waist level, or as close as possible to the body’s centerof mass, is often recommended [16], yet device placementremains a complex decision that is further complicated byissues relating to orientation and measurement artifacts [8].III. MOVEMENT MONITORING AND CLASSIFICATIONThe monitoring of human movement is a vast researchfield, with applications in gait analysis, rehabilitation,orthotic prescription, prosthesis adjustment, and orthopedicinterventions. Although movement classification may beregarded as a clinical application in its own right, for thepurpose of this review, it is presented in the following sectionbecause it actually forms much of the basis required to achieveambulatory monitoring in general. The work done in this fieldis paramount to providing a more in-depth understanding ofmovement on the whole, as well as generating classificationalgorithms that accurately identify WAM-recorded movements,without which one could not apply WAMs for the clinicalpurposes of falls detection, falls risk assessment and energyexpenditure measurement, all of which are discussed in asubsequent section of this review.The following section provides a summary of the majorstepping stones in activity classification research, fromthe basic identification of one’s posture, through commonpostural transitions, as well as gait and balance analysis.The section also presents the concept of using structuredmovements, which provide a priori knowledge during theclassification task. The section ends with a discussion regardingthe potential for performing movement monitoring in anunsupervised environment; a topic that overlaps in content withthe subsequent section of clinical applications.A. Posture and Transition IdentificationA basic level of movement monitoring entails adifferentiation between activity and rest. Accelerometry issuitable for this task since during rest only the gravitationalcomponent is recorded; the addition of a movement componentis relatively clear-cut when activity takes place, assumingcorrect signal preprocessing via filtering is executed. Thepremise employed is often that human motion will occupyactivities with a repetition rate above 0.1 Hz, or one repetitionper 10 s. This is a simple, but useful approximation whichallows the orientation of the device to be extracted, even in thepresence of other movement; however, changes in orientationfaster than this rate are not recognized, and what is calculatedamounts to an average device orientation.The next levels of activity classification involve identifyingbasic static positions (sitting, lying, and standing) followedby postural transitions (e.g., sit-to-stand and stand-to-lie)and even more complex dynamic activities, like walking andclimbing stairs. These tasks rely heavily on the extractionof appropriate features from the signal and utilization ofappropriate classifiers, which initially depend on correctsignal windowing and accurate segmentation [9]. Since the1990s, accelerometers and/or gyroscopes have been used forthis purpose successfully. Originally, more than one WAMwas used, often positioned on the chest and thigh [17],[18]. Subsequent improvements in sensor technology andclassification algorithms enabled the achievement of this goalwith greater accuracy using only one WAM, usually placed onthe chest [19] or waist [20]. Accelerometer-based WAMs stillshow relatively high error rates in differentiating postures suchas standing and sitting, as the angle of tilt from the vertical
    • SHANY et al.: SENSORS-BASED WEARABLE SYSTEMS FOR MONITORING OF HUMAN MOVEMENT AND FALLS 661axis varies by a nonsignificant margin between these statesand is particularly sensitive to the placement of the device. Apossible solution involves the use of gyroscopes, which havebeen proven accurate in identifying body transitions betweensitting and standing, whether as a standalone WAM as in [19],or in combination with accelerometers [21].B. Gait AnalysisBuilding on the basic ability to identify walking usingWAMs, attention was diverted to more advanced gait analysis.A gyroscope-based device was used in young and old subjectsto estimate a range of spatio-temporal gait parameters; reportedresults were comparable to those obtained with foot pressuresensors, which were regarded as the criterion standard [22].Using a triaxial accelerometer on the lower trunk, several teamswere also able to identify important gait parameters such aswalking speed, stride length, gait symmetry, and regularity [23],[24]. Additional popular locations for accelerometry-assistedgait analysis have been the upper trunk, head, tibia, and waist[25].The ability to obtain objective measurements of gaitparameters has been instrumental in comprehending gait-relateddifferences between healthy individuals and those sufferingwith Parkinson’s disease, neuropathy, or other disorders thataffect gait and balance. Moreover, a better understanding oftypical changes that occur in gait with aging (e.g., reducedwalking speed and a shorter step length) has also been achieved[25]. These research areas present an ongoing challenge andhave unique considerations. For example, measuring gaitparameters in individuals with sensorimotor deficits shouldtake into account abnormal segmental orientation. A possiblesolution may, once again, include a combination of sensormodalities; for example, in one case adding a gyroscope toan accelerometry-based WAM provided additional insightregarding segmental orientation and angular velocity [26].More complex walking scenarios, such as walking oninclined surfaces, remain a challenging classification task,particularly the identification of walking on mild slopes.Aminian et al. were the first to successfully estimate uphill anddownhill walking speed and incline. This was achieved usingtwo neural networks to analyze data from accelerometer-basedWAMs placed on the back (at waist level) and on top of the heel[27]. Using only one waist-mounted triaxial accelerometer,Wang et al. demonstrated that a Gaussian mixture modelapproach enables an overall classification accuracy of morethan 90% for walking on similar gradients, and even greaterthan 95% for the larger incline of [28]. This finding,and other work relating to walking in general and on unlevelterrains, is fundamental to the energy expenditure applicationdescribed here in Section IV-C.C. Balance and Sway TestingPostural sway and balance assessment using WAMs is an-other prolific research area. A proof-of-concept was providedin [29], where an accelerometer, placed in the back close to thecenter of mass, resulted in similar and even better results thana force platform, which is typically used for balance and swaymeasurements. Additional work related to WAM-associatedbalance measurements is included in the Falls Risk Assessmentand Prevention in Section IV-B.D. Directed Movements or RoutinesThe movements covered so far fit into the category ofactivities of daily living (ADL). More structured routinesare often used in order to control some of the unavoidablevariability in the way people conduct ADL, and to enable amore informed classification process. For example, subjectsmay be asked to perform a devised routine which is based ona set number of repetitions of sitting, standing, walking, andlying down that are performed in a certain order [11] or theymay be asked to walk on a certain path for a set distance [30].Whether involving WAM use or not, many researchersexamine how subjects perform specific controlled movements,such as:1) Sit-to-Stand Test (STS or STS5) – using a hard chair withno armrests, a seated person must stand and sit downagain as fast as possible, with their arms folded. This isperformed either one or five times, the latter version beingmore common. STS(5) is considered a measure of lowerlimb strength, speed and coordination [30].2) Alternate Step Test (AST) – subjects stand in front of astandard size platform (19 cm high, 40 cm wide), placeeach foot on the platform, and replace it back onto the flooras quickly as possible. This may be repeated several times,most commonly four times with each foot. The AST mayprovide a measure of lateral stability [30].3) Timed Up-and-Go Test (TUGT) – a seated subject rises to astanding position, walks three meters, turns around, returnsto the chair and sits back down as quickly as possible. Thus,overall this test combines aspects of STS transfers, walkingand turning [31].In addition to ADL routines and various controlledmovements, some routines combine several different elements.For example, Tinetti’s Performance Oriented MobilityAssessment includes STS transfers, balance control and a 360turn [32]. It should be noted that this test involves the examinerproviding a slight nudge to the subjects to off-balance them,and hence can only be performed in a supervised environment,whereas other directed routines may be self-administered oreven unsupervised. In addition, there is some subjectivityassociated with the force with which the examiner pushes thesubject off-balance.E. Unsupervised Movement MonitoringIndeed, much of the research involving WAM movementmonitoring is geared towards unsupervised use under reallife conditions. A pilot study involving unsupervised useof a waist-mounted accelerometry system by six elderlypeople was reported in 2004 [11]. Despite initial concernsof the aged subjects regarding the use of the technology,user compliance levels were high. The classification resultsshowed significant differences between activity and rest,and some ability to decipher between standing, sitting andlying. Fine-tuning of the classification algorithm resulted in100% accuracy in differentiating between activity and rest,94% accuracy for postural orientation recognition, 95.6% for
    • 662 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012TABLE ISUMMARY OF ACTIVITY CLASSIFICATION ARTICLES WITH SOME ADVANTAGES AND DISADVANTAGES LISTEDfalls detection, and 83.3% for correct walking classification[15]. A waist-worn device that combines a suite of sensors,including accelerometers for falls detection, was recentlytested in an unscripted trial in volunteers’ homes. The testingperiod however was limited to 8 h per person [4]. Nevertheless,most studies involving WAMs are still being conducted in asupervised manner in laboratories, hospitals or nursing homes.A comparative summary of some of the literature cited in thissection, relating to activity and posture classification, is listed inTable I.IV. CLINICAL USE OF WEARABLE AMBULATORY SENSORSSome of the main clinical applications of WAMs includefalls detection, falls prevention and risk assessment, as wellas physical activity and energy expenditure monitoring. Thefollowing section describes the motivation for each of theseapplications and sets the context for discussing some of themore notable or recently reported work in each area.A. Falls DetectionThe importance of detecting falls in order to prompt rapidassistance and prevent the consequences associated with“long lie” situations provides a fertile research ground. Theclassic personal alarm is not practical in situations where aperson is unable to press the emergency button due to lossof consciousness, injury, or emotional distress. Hence, anautomatic fall detection system, whether body-worn or as partof a smart home setup, is preferable, but only if its sensitivityand specificity levels are sufficiently high; it remains to bedetermined what the meaning of this is from a clinical, social,and economic perspective. Notably, automated falls detectioncould also be of value for more credible tracking of falls,which could serve several purposes, such as obtaining updatedand reliable epidemiological falls data or for a more objectivedetermination as to whether an individual may continue livingat home.Of particular concern in current systems is the high rate offalse alarms; i.e., misdetection of ADL or artifacts as a fall [33].Some systems offer the option of pressing a button to cancel afalse alarm, or in the case of a successful recovery [34]; there arealso suggestions of using an audio validation tool [15], but theseare once again irrelevant if a person is unable to interact withthe device. Thus, much effort is targeted towards improving theclassification algorithms that are intended to differentiate a fallfrom a normal movement, and furthermore to recognize a fallwith a successful or failed recovery prior to sending an alert.Using various WAMs (mostly accelerometers andgyroscopes), different approaches have been embraced forfalls detection. The most common techniques rely on thedetection of an impact with the ground, detection of an extremechange in body orientation towards a horizontal position, or asequential combination of the two; meaning that once an impactis noted, the algorithm also checks to determine the posturalposition [35]. Some algorithms may also employ a threshold fordetecting periods of inactivity following a suspected fall, withor without a recovery attempt [15]. Bourke et al. have recently
    • SHANY et al.: SENSORS-BASED WEARABLE SYSTEMS FOR MONITORING OF HUMAN MOVEMENT AND FALLS 663Fig. 1. Anteroposterior, mediolateral, and vertical accelerations, and baro-metric pressure, from a young healthy subject performing a simulated fall in theforward direction onto a mattress, whereby some attempt is made to break theimpact of the fall using the hands and arms [13]. The simulated fall is initiatedat approximately 25 s, and the fall event ends at around 32 s, with the timeof impact at about 28 s. While there is no large acceleration associated withthe fall, a sudden rise in the barometric pressure is observed, associated withthe new lower altitude of the subject now lying on the mattress. Multimodalsensor fusion of this type is becoming an emerging trend for unobtrusive andunsupervised monitoring devices.analyzed a number of combinations of acceleration magnitude,sensor velocity, and body posture to determine that a fusion ofall three features gives the highest fall sensitivity and the lowestfalse positive rate when using a triaxial accelerometer [36].In light of concerns that the WAM’s hardware could bedamaged by the impact of the fall to a degree which wouldrender it dysfunctional and unable to send an emergency alert,Bourke et al. have designed a pre-impact falls detection system.Using trunk accelerometry, vertical velocity profiles werefound to be sufficient for distinguishing falls from ADL witha specificity of 100% (by design, as a specific vertical velocitythreshold of was set) and a lead time of at least140 ms in comparison to an optical motion capture system[37]. An additional potentially important aspect of pre-impactfalls detection involves the suggested use of an inflatable hipand neck protector to reduce the risk of injury, in particular, hipfractures [38].Bianchi et al. recently tested a waist-mounted WAMcomposed of an accelerometer and a barometric pressuresensor. An array of fall scenarios were tested, both indoors andoutdoors, along with a series of ADL to test for false alarms[13]. Fig. 1 provides an example of a simulated fall event fromthis research. The subject falls forward and attempts to lessenthe impact by breaking the fall using their hands and arms.This is notable as fallers often make some attempt to breaka fall, however, many fall simulations refrain from testingthis particular scenario. The anteroposterior, mediolateral, andvertical accelerations are shown, along with the barometricpressure, which shows a rise when the fall occurs, associatedwith the subject’s new lower altitude.The accelerometry and air pressure signals are transformedto estimate the amount and intensity of subject movement, thedevice orientation, and the change in altitude. A heuristicallytrained decision tree classifier, involving a threshold-basedalgorithm, was utilized. An accuracy of 95%–100% wasobserved for identification of falls that end with recovery andeven difficult-to-identify scenarios such as collapsing into achair or resting and sliding against a wall to simulate the case ofa gradual loss of consciousness. Most importantly, the resultssuggest that adding a barometric pressure sensor may preventfalse positives under common scenarios of use [13]. However,like most studies reporting simulated falls, the subject cohortconsisted of healthy young adults. This system demonstratesa trend which is becoming common in many unobtrusivemonitoring systems currently under development, wherebythe deficits of the system which are imposed by its need tobe minimally invasive are compensated for through the fusionof many sensor modalities, which in itself becomes a morefeasible prospect with the decreasing cost and size of many ofthe sensors in question.The question of WAM location carries special significancein the context of the falls detection application since it entailslong-term continuous use, preferably also at night when manydangerous falls occur due to insufficient lighting and the needto visit the bathroom [39]. It was reported that in a three-monthhome trial with a case-enclosed waist-mounted accelerometer,users transferred the WAM between different body locationsdue to bruising and discomfort [11]. Ideally, WAM location andorientation would have no effect on measurement accuracy sothat users could attach it simply by themselves and relocate itas they wish. However, perhaps more realistic requirements arefor WAMs to at least be small and comfortable, not to causebruising or discomfort over time, even if attached continuouslyin the same place, and obviously not to cause injury to the wearerin the event of a fall. Moreover, a careful process of featureextraction would optimally lead to specific parameters that areindeed location and orientation-independent.Lindemann et al. suggested integrating an accelerometerinto a small hearing aid encapsulation that sits behind the ear.The reasoning was that people tend to avoid the uncomfortablefeeling associated with high accelerations at the head level;hence, if such accelerations are recorded, they are likelyunintentional and could signify a fall. Moreover, this locationis amenable to nighttime monitoring. Initial results seempromising, but the system was tested on only one young andone old volunteer [40].Testing of an accelerometer-based WAM, which wasincorporated into a lightweight vest, resulted in mediocreacceptance levels by ten elderly volunteers and staff at thenursing home where the testing was conducted. Even thoughno actual falls occurred during the four-week testing period,115 fall event warning messages were generated by the system,of which 42 where erroneously confirmed as “fall alerts” [33].Additional groups are working on smart garments, mostlyshirts, that would perhaps be easier to wear but have still notbeen validated for general use [41]. Such developments andtrials are highly valuable and may prove significant with time.It should be noted that current falls detection research ishindered by several inherent limitations. These include thefact that simulated fall scenarios are only performed by younghealthy volunteers; simulated falls do not necessarily representreal falls situations; and the falls are usually performed onto
    • 664 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012TABLE IISUMMARY OF FALLS DETECTION ARTICLES WITH SOME ADVANTAGES AND DISADVANTAGES LISTEDthick mats that provide a cushioning effect and alter thecharacteristics of the fall impact from that of a real fall. Klenket al. have recently shown distinct differences in the patternsexhibited between real and simulated falls [42]. In additionto this, there is great variety in terms of the types of WAMsbeing used, body locations, types of falls tested, and signalprocessing algorithms employed. Noury et al. addresses someof these issues and suggests a universal evaluation protocol for
    • SHANY et al.: SENSORS-BASED WEARABLE SYSTEMS FOR MONITORING OF HUMAN MOVEMENT AND FALLS 665WAM-based automated fall detection which includes specificmovement and fall scenarios, along with a recommendation torepeat each test three times per patient [43].A comparative summary of some of the literature cited in thissection, relating to falls detection, is contained in Table II.B. Falls Risk Assessment and PreventionIn light of the major negatives associated with falls, mucheffort is being dedicated towards their prevention. Clearly, thefirst step would be to single out those individuals most at risk. Inaddition, in light of the multifactorial nature of falling, it is alsobeneficial to identify the specific risks affecting each individualin order to provide the most suitable intervention.Falls risk assessment is a vast research area with widelydisparate approaches being used. There are various scoringsystems intended for use in hospitals, nursing homes, oroutpatient settings. The available indices are designed to beused by different professionals (e.g., geriatric doctors, nurses, orphysical therapists), and are based on questionnaires, observa-tions, physical examinations, or their combination. Some toolshave been reported in the context of only a specific subgroup ofthe elderly or chronically ill population, or were developed foruse in an individual facility. Reliability and credibility are alsoan issue in some instances [44].For many years, it has been noted that balance and gaitmaneuvers are most useful in recognizing recurrent fallers [45].Indeed, some falls risk assessment tools are based on functionalassessment tasks involving postural transfers and gait/balancetesting [44]. WAMs have been used successfully in objectivelyrecording and analyzing these elements of human motion; theyperhaps even perform better than some clinical measures ofbalance and require less or no equipment for the purpose of riskassessment [46]. Hence, there seems to be enormous potentialfor extending WAM use into the field of falls risk assessment.In spite of this, there has been only limited work performed inthis direction.Najafi et al. collected sit-to-stand and stand-to-sit (STS)measurements with a sternum-fixed gyroscope. Using adiscrete wavelet transform, they extracted several movementparameters, which were then correlated with the falling risk(low or high) of a small elderly subject population as determinedby an unvalidated customized fall risk score. The latter includeda combination of: Tinetti’s gait and balance assessment [32];history of falls; and visual, cognitive and mental disorders.The severity of the chosen STS features correlated with therisk allocation, but there was no evaluation of optimum cutoffpoints in the measured STS parameters that could independentlydiscriminate high-risk subjects [19]. STS parameters recordedusing an accelerometer-gyroscope combination on the chestwere also found to correlate ( ) with the Falls EfficacyScale, which assesses a subject’s confidence in executing ADLwithout falling and has been found to discriminate elderlyfallers and nonfallers [47].Additional work based on a comparison to the Tinettibalance scale was performed by Giansanti et al. who useda WAM composed of an accelerometer and a gyroscope tomeasure trunk sway of elderly subjects standing on firmand foam surfaces with eyes open and closed. Subjects wereFig. 2. Anteroposterior, mediolateral and vertical accelerations, low pass fil-tered at 2 Hz, from a subject performing the STS5 test, from Narayanan et al.[52]. Manually inserted event markers are shown (solid) which indicate whenthe subject started to move and when they returned to the seated position eachtime thereafter. The automatically determined markers, using the algorithm de-scribed by Redmond et al. [53], are also shown (lightly dashed).again allocated to either low or high risk categories withoverall accuracy via statistical clustering based onthe Mahalanobis distance as well as using a neural networkclassifier [48]. It should be noted that although balance deficitscan clearly increase an individual’s risk of falling, there aremany other fall risk factors that could come into play and whichare not captured by this balance-focused assessment tool [2].One must also consider the suitability of using balance testingin an unsupervised environment for risk of triggering falls. Aninteresting applicative idea involves balance rehabilitation viaan audio-feedback device which is based on alerting individualswith balance deficits when they sway beyond a set threshold[49]Menz et al. examined a more comprehensive array of gait andstability patterns in the elderly, as manifested at the head andpelvis levels, via accelerometry. Using the Physiological ProfileApproach (PPA) [50] for falls risk assessment, the authors wereable to define gait parameters on smooth and irregular surfaces,in particular the harmonic ratio, that differed between individ-uals with varied falls risk. However, once again, an optimalcutoff point for this parameter was not provided for independentrisk classification [51].The PPA was also used by Narayanan et al., who utilizeda different approach of allowing elderly subjects to perform adirected routine (STS5, AST and TUGT) in a semi-supervisedmanner while wearing a waist-mounted accelerometer. With theaddition of reaction time testing, a significant correlation of 81%was found between the overall PPA falls risk score and a certainset of extracted time-domain features. These features were alsocorrelated against the PPA subcomponents that include kneeextension strength, body sway, vision acuity, and proprioception[52]. The importance of this preliminary work lies in the factthat elderly subjects were able to self-administer a structurednon-ADL-based movement routine and that conclusions can bedrawn regarding individual deficits that increase one’s risk offalling, instead of just allocating a low or high risk classification.In a continuation of this work, Redmond et al. propose aheuristic method to segment the acceleration signals obtainedduring the execution of the directed routine. Segmentation of thesignals is required to identify key events in the directed routine
    • 666 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012TABLE IIISUMMARY OF FALLS RISK ASSESSMENT ARTICLES WITH SOME ADVANTAGES AND DISADVANTAGES LISTEDbefore features can be extracted from the waveforms to estimatethe risk of falling. In order for a fully unsupervised assessmentto be achievable, this segmentation process must be automated[53].Fig. 2 shows an illustrative example from the study byNarayanan et al. of the acceleration traces acquired by a triaxialaccelerometer during the STS5 test [52]. Shown are the eventmarkers, inserted in real-time by the observing researcher, andthe corresponding automatically determined event markers asestimated using the algorithm described by Redmond et al.[53]. The event markers indicate the moment when the subjectbegan to rise from the chair, and the time when they returned tothe seated position each time thereafter. It is somewhat evidentfrom the nature of these signals that if some estimate of fallsrisk is to be gleaned from signals such as these, which areobtained in quite an unobtrusive manner using a WAM, themovements must be of a constrained type (in this case the STS5test). It is feasible that completely undirected movement couldbe employed to estimate a risk of falling, whereby the subjectis not required to perform any such scheduled assessment;however, the monitoring period would likely need to be overa much longer duration, such that patterns indicative of apredisposition to fall might become evident; e.g., the averageamount of movement seen in the day or the speed at which theymove through the environment. The added advantage of such amonitoring approach would be that compliance with the needto perform regular assessment such as the directed routine is nolonger an issue.A rather important study was reported by Marschollek et al.who combined TUGT accelerometry parameters with geriatricassessment data (STRATIFY and Barthel Index) to demonstratethe added benefit of WAM measurements in predicting falls.Uniquely, the risk stratification of the subject population in thistrial was based on collected credible falls data, rather than onvalidation against a risk score [31].More recently, Greene et al. employed two triaxialaccelerometer and gyroscope devices attached to the lowerlegs to estimate risk of falling of subjects executing the TUGT.
    • SHANY et al.: SENSORS-BASED WEARABLE SYSTEMS FOR MONITORING OF HUMAN MOVEMENT AND FALLS 667Results show improvement in retrospectively predicting falls incomparison to the normal manually timed TUGT or the BergBalance Scale. However, since the devices must be carefullyplaced on the shanks, it may unsuitable for unsupervised homeuse [54].An interesting approach was adopted by Weiss et al. whodeveloped an algorithm for automated detection of near fallsusing a pelvic-trunk accelerometer. The hypothesis behindthis work is that near falls may hold some predictive valueof a subject’s risk of falling. Since near falls situations occurmore frequently than actual falls, monitoring of near falls mayprove advantageous due to the shorter time period requiredto reach conclusions; for example, in a prospective trialintended to assess falls risk via collection of real falls statistics.The reported algorithm had accuracy, but this worknevertheless demonstrated that accelerometry can successfullyidentify near falls [55].A comparative summary of some of the literature cited in thissection, relating to falls risk estimation, is contained in Table III.C. Energy ExpenditureAnother important trend in society is an increased awarenessto problems like obesity and lack of physical exercise, whichhave reached epidemic proportions [1]. Monitoring andintervention are sought not only for weight reduction andincreasing activity levels, both in children and adults; they arecrucial in preventing an even further increase in the prevalenceof chronic illnesses such as diabetes and cardiovasculardisorders. Physical activity quantification is also essential inother clinical contexts (for example, rehabilitation followingsurgery or stroke).Energy expenditure (EE) is considered a parameter ofimportance with regards to physical activity quantification. Thework published by Bouten et al. in the 1990s defined muchof the basis for employing accelerometers for EE estimationthanks to the finding of a linear relationship between theintegrals of the accelerometry signal magnitudes and the actualEE measures [56]. Additional work since then, as well asprogress in movement classification, has improved the accuracyof detecting the type, duration, and intensity of the physicalactivity performed [57]. Much of EE-related research hasfocused on walking, since it is viewed as the single largestcontributor to daily EE, even in the elderly [58].Notably, the incorporation of a barometric pressure sensorto accelerometer-based WAMs has been found to serve asa surrogate measure to altitude, providing an advantageparticularly with regards to identifying stairs ascent/descent[59], [60]. This is important, as more energy is expendedwhen walking up or down stairs than when walking on a flatterrain [61]. Voleno et al. estimated oxygen consumption usingvarious features extracted from the accelerometry signals andthe time-derivative of the barometric air pressure signal [60].A linear black-box model was employed and was optimizedby searching for the optimal subset of input features as wellas model poles, zeros, and delays. This modification improvedthe overall fit to a gold standard measure of oxygen uptake.However, this was performed in young healthy adults and mustbe validated in subjects of more advanced age or in specifictargeted disease populations.Thus, WAMs may potentially replace the costly andcumbersome methods of doubly labeled water and indirectcalorimetry, which have been considered the gold standardtechniques for EE application but are not practical for everydayuse. WAMS also offer an objective and easy-to-use alternativeto physical activity questionnaires. However, WAM applicationfor EE still suffers from some limitations, such as correctlyaccounting for: EE by upper body and arms, EE duringsedentary activities (unless proven negligible), EE whencarrying physical objects, and EE when walking on irregularsurfaces.V. DISCUSSIONTelehealth solutions comprise the basis of an attractive modelfor the treatment of chronic diseases, such as chronic heartfailure or chronic obstructive pulmonary disorder. As a result,tremendous research efforts, including intergovernmentalprojects such as CAALYX in Europe, are directed towardsthe maturation of such applications [4]. However, due to thecomplexity of the targeted diseases and existing treatmentnetworks which surround them, there will likely be aconsiderable lead time in the uptake of new care models.Sensor-based detection and possible prevention of falls, as wellas the monitoring of physical activity in general, are also wellsuited to the telehealth care strategy and may be more quicklyintegrated into the clinical arena due to a reduced complexityin the care delivery model required. In this paper, we haveprovided a review of ambulatory monitors and their clinicalapplications, with an emphasis on those associated with themonitoring of falls and the estimation of falls risk.Diagnosis of deficits that could signify increased falls riskin elderly and chronically ill individuals may be achievedvia one-off testing in a clinic or unsupervised ambulatorymonitoring using body-worn sensor technology. Thistechnology is also applicable in long-term monitoring at homefor the purpose of generating emergency alerts following severefalls. However, some hurdles that might unfortunately impedecurrent and future research of WAMs should be addressed.An extreme variation exists in the types of sensors used, theircombinations, body locations, features extracted for analysis,and validation tools. While it is clearly positive to examine allpossible aspects of WAM use in order to determine the optimalsystems and implementation protocols, it also seems that someconstructive convergence of research directions is required andwould be advantageous. For example, falls detection researchwould indeed benefit from adoption of a common definitionin order to allow some buildup of a meaningful data pooltowards valuable meta-analyses. A universal protocol for fallsdetection evaluation has been suggested [43] and is a step in theright direction. Future research may always include additionalaspects beyond the scope of a common testing protocol, butshould aim to incorporate such a protocol at a minimum. Thiscould lead in turn to a unified choice of the most appropriatesensor or, perhaps more likely, sensor combination. Indeed,the reported research suggests that an integrated device which
    • 668 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012enables the fusion of various sensor types and data sourceswould be superior. For example, the inclusion of an additionalfeature of barometric pressure greatly improves the specificityof an accelerometer-based WAM by reducing the numberof false positive detections, an issue which plagues manyaccelerometry-based falls detection devices.This disarray of ideas is also evident when reviewing thereported research regarding WAM-based falls risk assessment,which seems to have fallen into the pitfall affecting fall riskassessment in general. Various WAM types are used in manyways to generate some measure of relative risk, which is thenvalidated against different assessment tools, some of which areonly partially validated themselves, or rely on subjective data.It is indeed difficult to obtain a gold standard against which tovalidate a suggested risk assessment. The PPA, for example, is arelatively well-validated tool that has been used for thousands ofpatients globally, but even it claims a falls prediction accuracyof only 75%–80% [50]. Clearly, in order to develop a falls riskassessment tool one must verify its accuracy against a reliablefalls history, as done in [31] for hospital in-patients, or via awell-designed prospective study. Interestingly, when performedin an unsupervised environment for home-dwelling individuals,even a prospective trial is not a gold standard, as it would rely onself-reported falls which are known to possess credibility faults[8].Most falls risk assessment research has focused on the abilityto distinguish between elderly people who fall versus nonfallersor even between fallers and repeat fallers. While clearly ofimportance, it would be far more beneficial to identify specificfactors that increase one’s fall risk, enabling more customizedintervention. In light of the available knowledge base ofobjective fall risks, a good starting point would perhaps beto define a limited set of movements that relate to criticalaspects of fall risks, can be captured well by WAMs, and haveextractable features that map back to the chosen fall risks.Having such a directed routine a priori would simplify mattersfrom a movement classification perspective. The feasibility ofhaving elderly people perform a given movement routine, evenif unsupervised, has been established [11], [52]. In choosingthe motion set, one must exercise caution in refraining frommovements that may provide important details related to fallsrisk, but might trigger a fall; this is of even greater importancewhen considering the end goal of unsupervised use.It should also be considered that, as a standalone tool, aWAM-based falls risk assessment may provide insight onlyregarding individuals with certain risk factors, but due to themultifactorial nature of falling it might miss individuals whoserisk factors lie in other areas. For example, it is unknownhow the prominent risk factor of fear of falling [62] would becaptured by a WAM assessment tool, if at all. Nevertheless,in light of the staggering statistics of falls, even if offeringonly a partial solution, a quick, simple and objective falls riskassessment tool is of vital importance.VI. 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Howley, “The oxygenrequired for horizontal and grade walking on a motor-driven treadmill,”Med. Sci. Sports & Exercise, vol. 17, pp. 640–645, 1985.[62] K. Delbaere, J. C. T. Close, H. Brodaty, P. Sachdev, and S. R. Lord,“Determinants of disparities between perceived and physiological riskof falling among elderly people: Cohort study,” BMJ, vol. 341: c4165,2010.Tal Shany received the B.Sc. (Hons) degree inbiology from the Hebrew University of Jerusalem,Jerusalem, Israel, in 1999, and the M.Sc. degree inmedical sciences from the Israel Institute of Tech-nology, Haifa, in 2002. After specializing in clinicaltrials for the medical device industry, she is currentlyworking towards the Ph.D. degree at University ofNew South Wales (UNSW), Sydney, Australia.Her interests include telehealth applications for theelderly and chronically ill.
    • 670 IEEE SENSORS JOURNAL, VOL. 12, NO. 3, MARCH 2012Stephen J. Redmond (M’07) received the B.E.(Hons) degree in electronic engineering and thePh.D. degree in biomedical signal processing fromthe National University of Ireland, Ireland, Dublin,in 2002 and 2006, respectively.He is currently a Lecturer at the Graduate Schoolof Biomedical Engineering, University of New SouthWales (UNSW), Sydney, Australia. His research in-terests include pattern recognition, biomedical signalprocessing, telehealth, and machine intelligence.Prof. Redmond is a Reviewer for the IEEETRANSACTIONS ON BIOMEDICAL ENGINEERING and the IEEE TRANSACTIONSON INFORMATION TECHNOLOGY IN BIOMEDICINE.Michael R. Narayanan (M’10) received the B.E.(Hons) degree in computer engineering, the M.E.degree in biomedical engineering in 2005, andthe Ph.D. degree in electrical engineering in 2011from the University of New South Wales (UNSW),Sydney, Australia.He is a Postdoctoral Researcher with the Biomed-ical Systems Laboratory, UNSW, Sydney, Australia.His interests include hardware design, wireless mon-itoring systems, and falls research.Nigel H. Lovell (M’90–SM’98–F’11) received theB.E. (Hons) and Ph.D. degrees from the Universityof New South Wales (UNSW) Sydney, Australia.He is currently a UNSW Scientia Professor ofBiomedical Engineering with the Graduate Schoolof Biomedical Engineering and holds an AdjunctProfessorship in the School of Electrical Engineeringand Telecommunications. He has authored 300+refereed journals, conference proceedings, bookchapters, and patents. His research work has coveredareas ranging from cardiac modeling, home tele-health technologies, biological signal processing, and visual prosthesis designhaving been awarded over $68 million in research grants.Dr. Lovell was the IEEE Engineering in Medicine and Biology Society(EMBS) Vice President (VP) for Conferences (2004/2005 and 2010/2011) andVP for Member and Student Activities (2002/2003). He was the ScientificCo-Chair for the Annual IEEE EMBS Conference in Lyon in 2007 and wasawarded the IEEE Millennium Medal for services to the EMBS and theprofession.