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A proposal for elderly frailty detection by using accelerometer-enabled smartphones


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Presentation at UCAmI 2011.

Published in: Technology

A proposal for elderly frailty detection by using accelerometer-enabled smartphones

  1. 1. A proposal for elderly frailty detection by using accelerometer- enabled smartphones J. Fontecha, R. Hervás, F.J. Navarro, L. Sánchez, J. Bravo University of Castilla-La Mancha, Spain Geriatric Services of Retirement home MAmI Research Lab Ciudad Real, Spain
  2. 2. Frailty Overview Resistance and physiological Adverse health Cumulative wear reserves decrease effectsFrailty Overview Frailty stateGoal and MotivationFrailty Detection Architecture Data acquisition • In most of cases is related to aging, disability and comorbidity. Pattern analysis Clinical Factors • An adult person may have the first symptoms of pre-frailty Instantiation after 65 years old.Instances Comparison • Hetereogeneous population: there are people who reachArchitecture Flow advanced ages of your life with good health. 2Conclusions
  3. 3. Frailty Overview • Most common clinical manifestations: Involuntary reduction Altered biological of body weigh markers Decline in Decreased endurance Balance and gait physical mobility & muscle strength disorders • Current frailty detection methods: Assessment of Activities of daily living (ADL) and instrumental activities of daily living (IADL) Barthel index, Lawton &Frailty Overview Brody test,… However…Goal and MotivationFrailty Detection Architecture Physician viewpoint Assessment of Physical contidion and Data acquisition gait  tinetti test, Get-Up and go test,… Pattern analysis Clinical Factors Assessment of patient record  clinical Instantiation indicators analysisInstances ComparisonArchitecture Flow 3Conclusions
  4. 4. Goal and Motivation • Goals • Developing a mobile architecture to make a more accurate diagnosis of frailty state by using accelerometer-enabled smartphones. • Using principles of inclusion and mobile computing to integrate the system in a health care environment. • Motivations Patient record • Frailty detection with current methods is poor and subjective.Frailty OverviewGoal and Motivation • Related works propose mechanisms Accelerometer outputFrailty Detection Architecture for movement recognition, gait Data acquisition study, assessment of fall risk,… but Pattern analysis Clinical Factors These don’t take into account clinical Instantiation factors of the patient record.Instances ComparisonArchitecture Flow 4Conclusions
  5. 5. Frailty Detection Architecture • Our architecture is divided into four stages: • Data acquisition • Collecting movement data by using a mobile device with accelerometer mechanisms. Stage 1 • Establishing a stack of reference items • Pattern analysis • Analysis and study of movement data. Signal segmentation and filtering. Stage 2 •Calculation of dispersion measures. • Clinical Factors • Collecting a set of clinical factors related to frailty from the patient record.Frailty Overview Stage 3 • Quantification and discretization of influential variables.Goal and MotivationFrailty Detection Architecture Data acquisition • Instantiation Pattern analysis • Creation and instance comparison. Clinical Factors Stage 4 • Improving the accuracy of the system. InstantiationInstances ComparisonArchitecture Flow 5Conclusions
  6. 6. Data Acquisition • Tipically, physical condition assessment for frailty is performed through 2 tests: • Assess gait and balance • Assess balance • Elder walk for a few meters • Steps from a sitting position Tinetti • Geriatrician gives a score • stand up • symmetry • walk several paces • flow • turn Get-Up and Go • return to the chair • path • speed • Geriatrician provides a ratingFrailty OverviewGoal and Motivation Fig 1. Device position at the waistFrailty Detection Architecture Data acquisition Pattern analysis • In this stage of our architecture, an Android app is running on Clinical Factors the mobile phone which is attached to the elder waist for InstantiationInstances Comparison collecting movement data during the exercise (see fig. 1).Architecture Flow 6Conclusions
  7. 7. Pattern Analysis • The Android software performs an analysis of the previous movement data. This stage is divided into 3 parts: • Mobile device stores movement data in a text file • Sampling frequency: 20Hz • X, Y and Z coordinates • Datetime Movement • Kind of exercise data Storage • Segmentation. Aplication of effective sample time algorithm, created for: • Deleting invalid movement valuesFrailty Overview • Determining a valid range timeGoal and Motivation Segmentation • Filtering. Remove the noise and signal smoothingFrailty Detection Architecture and filtering • Application of low-pass filter Data acquisition Pattern analysis • Calculation of a set of relevant dispersion measures (for the Clinical Factors three coordinate axes) Instantiation • We work in the time domain: it isn’t required a highInstances Comparison processing capacityArchitecture Flow Dispersion measures • Used for data classification and data miningConclusions calculation 7
  8. 8. Clinical Factors • Physical condition is an important component, but it isn’t enough for frailty diagnosis. • We need results of clinical factors • Seven groups of these parameters have been identified General Info Gender, age, size, weight, BMI, drug number Functional assessment Tinetti gait score, Tinetti balance score, Barthel index, Lawton score, Help,… Nutritional Assessment Total protein, serum albumin, cholesterol level, blood iron, vitamin B12,…Frailty Overview Dementia, depression, incontinence, immobility, recurrent falls, Geriatric SyndromesGoal and Motivation comorbidity,…Frailty Detection Architecture Data acquisition Independence in ADL Independent, mild dependence, moderate dependence, serious Pattern analysis dependence,… Clinical Factors Instantiation Cognitive assessment MiniMental status, CRPInstances ComparisonArchitecture Flow Pathologies and Diseases 8Conclusions
  9. 9. Instantiation • All variables from physical and clinical study are known as influential variables • Instance  A set of influential variables associated with an elderly person and a specific Instance exercise, at a given moment. Id. instance Id. user Activity Timestamp Influential variablesFrailty OverviewGoal and Motivation Accelerometer Data Clinical FactorsFrailty Detection Architecture Data acquisition Pattern analysis Mean Variance Amplitude Acceleration St. Deviation Others Clinical Factors InstantiationInstances Comparison General info Functional assessment Nutritional assessment Cognitive assessment …Architecture Flow … … … 9Conclusions Vitamin B12 Others
  10. 10. Instances Comparison • After instantiation, the instances are compared. … Instance stack New instance • Procedure based on Affinity Degrees between instances. Diagnosis support Indications • Influential variables are compared Recomendations by means of equality andFrailty Overview similarity coincidences.Goal and Motivation • Equality  Influential variables to be compared have theFrailty Detection Architecture Data acquisition same measurable value or not. (e.g. Tinetti gait score) Pattern analysis Clinical Factors • Similarity  Influential variables are compared based on Instantiation a range of similarity (physician and system defineInstances Comparison maximum and minimum range values) (e.g. accelerationArchitecture FlowConclusions values) 10
  11. 11. Instances Comparison • The mobile system implements an Affinity Tree based on the previous comparison results. • Content of the tree  The most relevant instances according to the affinity degrees. (1) High level of affinity • The root node Root (2) Medium level of affinity represents the current instance to compare. (1) • The rest of instances are obtained by means of the comparison (2)Frailty Overview method.Goal and Motivation Affinity tree with the maximum children numberFrailty Detection Architecture Data acquisition • Two levels (high and medium) are been established to make run Pattern analysis Clinical Factors faster and easier to interpret results on the mobile phone. Deeper Instantiation levels are not necessary.Instances ComparisonArchitecture Flow • Mobile device uses these information to create charts, indications,Conclusions recommendations,… to support physician diagnosis. 11
  12. 12. Architecture FlowFrailty OverviewGoal and Motivation • Nowadays, the system is beingFrailty Detection Architecture Data acquisition evaluated on 20 elderly people at a Pattern analysis retirement home, but a first Clinical Factors approach of evaluation have been InstantiationInstances Comparison satisfactory results.Architecture Flow 12Conclusions
  13. 13. Conclusions • Integration of mobile devices in healthcare environments, in a natural way. • The use of mobile device capabilities to detect and diagnose pre-frailty and frailty state in elderly. • The creation of methods and mechanisms to get and calculate relevant data, from accelerometer sensor and patient record. • Offering an essential support to geriatricians to determine frailty in a objective way. • Providing a set of results to establish recommendations,Frailty Overview indications and relevant information about future pathologies.Goal and Motivation • We are considering the development of a mobile platform forFrailty Detection Architecture Data acquisition monitoring daily activities by using our architecture. Pattern analysis Clinical Factors InstantiationInstances ComparisonArchitecture Flow 13Conclusions
  14. 14. A proposal for elderly frailty detection by using accelerometer- enabled smartphones J. Fontecha, R. Hervás, F.J. Navarro, L. Sánchez, J. Bravo University of Castilla-La Mancha, Spain Geriatric Services of Retirement home MAmI Research Lab Ciudad Real, Spain