Enhancing the measurement of clinical outcomes using Microsoft Kinect choices (Philip Breedon, Bill Byrom, Luke Siena and Willie Muehlhausen)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
RETINA COMPANY SHOWCASE - TrueVision SystemsHealthegy
Presentation by TrueVision Systems at OIS@ASRS 2016.
Participant:
Forrest Fleming, CEO - TrueVision Systems
Powered by:
Healthegy
For more ophthalmology innovation
Visit us at www.ois.net
A musculoskeletal model driven by microsoft kinect sensor v2 dataAdam Frank
ABSTRACT
Objective. To develop a musculoskeletal model driven by data retrieved from Microsoft Kinect Sensor v2 and compare the output to a musculoskeletal model driven by data from makerbased motion capture system for three different movements. Furthermore, determine the optimal position for the Microsoft Kinect Sensor v2 for each movement.
Method. In the positioning test, a combination of seven angles, three heights and three distances was conducted to find the optimal position for obtaining data for a musculoskeletal model, doing a gait, squat and shoulder abduction cycle. When the optimal positions for the three different movements were determined, data for the comparison test were collected for five healthy male subjects. Eight Oqus 1 infrared high-speed cameras and two force platforms were used to collect the maker-less based motion capture data. One Microsoft Kinect Sensor v2 was used to collect the marker-less based motion capture data. AnyBody Modeling System was used to analyze different variables for the two systems.
Results. Multiple positions were fund to be optimal for the position of the Microsoft Kinect Sensor v2 at the squat and the shoulder abduction movement. The same positions for these movements were chosen to be the same (0°, 0.75/2.6). The optimal position for the gait movement (0°, 0.75m/3.4m) was determined, based on the highest percentage of tracked Kinectjoints. Strong correlations were found in the comparison test for knee flexion angle and hip flexion angle for both the gait and the squat movement. Doing the shoulder abduction movement, strong correlations were found for shoulder abduction angle (0.99) and moment (0.88). Even though strong correlation were found in the ankle flexion angle (0.71) in the squat movement, other results indicates that the Microsoft Kinect Sensor v2 has limitations tracking the ankle sufficiently. A strong correlation in the ground reaction force (0.81) was observed for the gait movement, where as the ground reaction forces in the squat movement were: left (0.49) and right (0.50).
Conclusion. The results of this study show that data obtained by the Microsoft Kinect Sensor v2 can be used as input in a musculoskeletal model. Though the Microsoft Kinect Sensor v2 show some encouraging results for some variables, it still proves insufficient as a alternative to marker-based systems.
Io t and cloud based computational framework, evolutionary approach in health...owatheowais
The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.
These slides use ideas from my (Jeff Funk) class to develop a business model for MYO’s new form of computer interface. This wrist band-based device uses EMG signals from a person’s forearm to control mobile phones, MP3 players, and other electronic devices. This enables users to interact with electronic devices in ways that can’t be done with existing gesture (e.g., Kinect, LEAP)-based devices or touch screens. Snow boarders can control their MP3 players on the slopes while gamers can do new tricks.
Embedded Implementations of Real Time Video Stabilization Mechanisms A Compre...YogeshIJTSRD
Video Stabilization has been widely researched and still is under active research considering the advancements that are being done in the field of digital imagery. Despite all the works, Hardware based Real time Video Stabilization systems especially works dealing with implementation of Technology on prototype Implementation boards have been very few. This review works focuses on the specific aspect of the modern day Stabilization systems implemented in prototyping boards and the algorithms that have been found suitable for such implementation taking considerations of cost, size and speed as the principal criterions. Mohammed Ahmed | Dr. Laxmi Singh "Embedded Implementations of Real Time Video Stabilization Mechanisms: A Comprehensive Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39963.pdf Paper URL: https://www.ijtsrd.com/engineering/other/39963/embedded-implementations-of-real-time-video-stabilization-mechanisms-a-comprehensive-review/mohammed-ahmed
RETINA COMPANY SHOWCASE - TrueVision SystemsHealthegy
Presentation by TrueVision Systems at OIS@ASRS 2016.
Participant:
Forrest Fleming, CEO - TrueVision Systems
Powered by:
Healthegy
For more ophthalmology innovation
Visit us at www.ois.net
A musculoskeletal model driven by microsoft kinect sensor v2 dataAdam Frank
ABSTRACT
Objective. To develop a musculoskeletal model driven by data retrieved from Microsoft Kinect Sensor v2 and compare the output to a musculoskeletal model driven by data from makerbased motion capture system for three different movements. Furthermore, determine the optimal position for the Microsoft Kinect Sensor v2 for each movement.
Method. In the positioning test, a combination of seven angles, three heights and three distances was conducted to find the optimal position for obtaining data for a musculoskeletal model, doing a gait, squat and shoulder abduction cycle. When the optimal positions for the three different movements were determined, data for the comparison test were collected for five healthy male subjects. Eight Oqus 1 infrared high-speed cameras and two force platforms were used to collect the maker-less based motion capture data. One Microsoft Kinect Sensor v2 was used to collect the marker-less based motion capture data. AnyBody Modeling System was used to analyze different variables for the two systems.
Results. Multiple positions were fund to be optimal for the position of the Microsoft Kinect Sensor v2 at the squat and the shoulder abduction movement. The same positions for these movements were chosen to be the same (0°, 0.75/2.6). The optimal position for the gait movement (0°, 0.75m/3.4m) was determined, based on the highest percentage of tracked Kinectjoints. Strong correlations were found in the comparison test for knee flexion angle and hip flexion angle for both the gait and the squat movement. Doing the shoulder abduction movement, strong correlations were found for shoulder abduction angle (0.99) and moment (0.88). Even though strong correlation were found in the ankle flexion angle (0.71) in the squat movement, other results indicates that the Microsoft Kinect Sensor v2 has limitations tracking the ankle sufficiently. A strong correlation in the ground reaction force (0.81) was observed for the gait movement, where as the ground reaction forces in the squat movement were: left (0.49) and right (0.50).
Conclusion. The results of this study show that data obtained by the Microsoft Kinect Sensor v2 can be used as input in a musculoskeletal model. Though the Microsoft Kinect Sensor v2 show some encouraging results for some variables, it still proves insufficient as a alternative to marker-based systems.
Io t and cloud based computational framework, evolutionary approach in health...owatheowais
The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.
These slides use ideas from my (Jeff Funk) class to develop a business model for MYO’s new form of computer interface. This wrist band-based device uses EMG signals from a person’s forearm to control mobile phones, MP3 players, and other electronic devices. This enables users to interact with electronic devices in ways that can’t be done with existing gesture (e.g., Kinect, LEAP)-based devices or touch screens. Snow boarders can control their MP3 players on the slopes while gamers can do new tricks.
Embedded Implementations of Real Time Video Stabilization Mechanisms A Compre...YogeshIJTSRD
Video Stabilization has been widely researched and still is under active research considering the advancements that are being done in the field of digital imagery. Despite all the works, Hardware based Real time Video Stabilization systems especially works dealing with implementation of Technology on prototype Implementation boards have been very few. This review works focuses on the specific aspect of the modern day Stabilization systems implemented in prototyping boards and the algorithms that have been found suitable for such implementation taking considerations of cost, size and speed as the principal criterions. Mohammed Ahmed | Dr. Laxmi Singh "Embedded Implementations of Real Time Video Stabilization Mechanisms: A Comprehensive Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39963.pdf Paper URL: https://www.ijtsrd.com/engineering/other/39963/embedded-implementations-of-real-time-video-stabilization-mechanisms-a-comprehensive-review/mohammed-ahmed
Evaluating the Microsoft Kinect for use in Upper Extremity Rehabilitation Following Stroke as a Commercial off the Shelf Gaming System. The Therapist’s Perspective by Luke Shires, David Brown, Nasser Sherkat, James Lewis and Penny Standen
Emerging technologies in physical therapy and rehabilitation: 10 opportunitie...Healthstartup
New technology solutions that integrate multiple sensors (such as body tracking), interfaces (virtual reality) and games promise to fundamentally transform, if not disrupt the field of physical therapy and rehabilitation. This presentations shows 10 opportunities for startups and clinicians to make a difference today.
Iotswc2016 - Microsoft - Healthcare track - lombardi - KHAREallo75
KHARE is a rehabilitation solution to speed up the recovery process for injured people: using Kinect, Microsoft Band and an IoT architecture on Azure, the human telemetry supports physiatrists and neuroscience researchers in tracking movements of exercises and predicting rehab trends. The body telemetry of a patient allows for experimentation with new rehabilitation modalities using mirror neurons.
* This is the presentation made at IoT Solution World Congress (IOTSWC) 2016 in the healthcare track
A Distributed System Using MS Kinect and Event Calculus for Adaptive Physioth...Stefano Bragaglia
In many countries of the world, the life expectancy increases but the population ages so rapidly that it is expected that soon it will be difficult to ensure a good life quality to the elder people when health issues arise. In this paper, we consider this problem from the point of view of the physiotherapy rehabili- tation which nowadays is perceived as costly and inconvenient for the elder patients. In order to lessen these problems, we propose a distributed architecture to allow the physiotherapists to remotely assist their patients while they comfortably do exercises from home. As in other proposals, the Human Pose Recognition is delegated to a computer equipped with MS Kinect and neural networks. Our approach, however, differs from others because it includes a logical framework based on Event Calculus augmented with Expectations which provides a higher-level description of the exercises and a mean to measure how well they were done.
Attention Approximation: From the web to multi-screen televisionCaroline Jay
The move towards the provision of television content over two or more screens represents an enormous opportunity and a considerable challenge. A scientific understanding of what causes people to switch attention between the main screen and a 'second screen' mobile device during television viewing is key to the development of this technology. This seminar describes how ‘attention approximation’, a technique we have used to model visual attention and design screen reader presentation of Web content, can be used to investigate viewing behaviour, and ultimately drive the provision of content across multiple screens.
Measuring the Drop Vertical Jump using the Microsoft Kinectthegraymatters
Validation and Pilot Testing of a Portable and Inexpensive ACL Injury Risk Identification Tool: Measuring the Drop Vertical Jump using the Microsoft Kinect
Evaluating the Microsoft Kinect for use in Upper Extremity Rehabilitation Following Stroke as a Commercial off the Shelf Gaming System. The Therapist’s Perspective by Luke Shires, David Brown, Nasser Sherkat, James Lewis and Penny Standen
Emerging technologies in physical therapy and rehabilitation: 10 opportunitie...Healthstartup
New technology solutions that integrate multiple sensors (such as body tracking), interfaces (virtual reality) and games promise to fundamentally transform, if not disrupt the field of physical therapy and rehabilitation. This presentations shows 10 opportunities for startups and clinicians to make a difference today.
Iotswc2016 - Microsoft - Healthcare track - lombardi - KHAREallo75
KHARE is a rehabilitation solution to speed up the recovery process for injured people: using Kinect, Microsoft Band and an IoT architecture on Azure, the human telemetry supports physiatrists and neuroscience researchers in tracking movements of exercises and predicting rehab trends. The body telemetry of a patient allows for experimentation with new rehabilitation modalities using mirror neurons.
* This is the presentation made at IoT Solution World Congress (IOTSWC) 2016 in the healthcare track
A Distributed System Using MS Kinect and Event Calculus for Adaptive Physioth...Stefano Bragaglia
In many countries of the world, the life expectancy increases but the population ages so rapidly that it is expected that soon it will be difficult to ensure a good life quality to the elder people when health issues arise. In this paper, we consider this problem from the point of view of the physiotherapy rehabili- tation which nowadays is perceived as costly and inconvenient for the elder patients. In order to lessen these problems, we propose a distributed architecture to allow the physiotherapists to remotely assist their patients while they comfortably do exercises from home. As in other proposals, the Human Pose Recognition is delegated to a computer equipped with MS Kinect and neural networks. Our approach, however, differs from others because it includes a logical framework based on Event Calculus augmented with Expectations which provides a higher-level description of the exercises and a mean to measure how well they were done.
Similar to Enhancing the measurement of clinical outcomes using Microsoft Kinect choices (Philip Breedon, Bill Byrom, Luke Siena and Willie Muehlhausen)
Attention Approximation: From the web to multi-screen televisionCaroline Jay
The move towards the provision of television content over two or more screens represents an enormous opportunity and a considerable challenge. A scientific understanding of what causes people to switch attention between the main screen and a 'second screen' mobile device during television viewing is key to the development of this technology. This seminar describes how ‘attention approximation’, a technique we have used to model visual attention and design screen reader presentation of Web content, can be used to investigate viewing behaviour, and ultimately drive the provision of content across multiple screens.
Measuring the Drop Vertical Jump using the Microsoft Kinectthegraymatters
Validation and Pilot Testing of a Portable and Inexpensive ACL Injury Risk Identification Tool: Measuring the Drop Vertical Jump using the Microsoft Kinect
ToBITas Case Study, Presentation for UCAMI 2014 conferenceBorja Gamecho
This is the presentation for the paper: "Evaluation of a Context-Aware Application for Mobile Robot Control Mediated by Physiological Data: The ToBITas Case Study."
In the UCAMI 2014 conference (2 to 5 of December).
Similar to Enhancing the measurement of clinical outcomes using Microsoft Kinect choices (Philip Breedon, Bill Byrom, Luke Siena and Willie Muehlhausen) (20)
Robotics and Education – EduRob Project Results Launch
10:45 Introduction to the EDUROB Project (Professor Penny Standen)
11:00 Robotic Learning Demos (Andy Burton, Nick Shopland, Steve Battersby)
11:30 Robots in Schools – initial findings (Joanna Kossewska, Lorenzo Desideri) See also ‘Education of children with disabilities using NAO robot mediation – the Polish experience’ - Joanna Kossewska, Elżbieta Lubińska-Kościółek, Tamara Cierpiałowska, Sylwia Niemiec-Elanany, Piotr Migo, Remigiusz Kijak (Pedagogical University of Krakow, Poland)
12:00 Interactive hands-on sessions with the robots
12:30 Discussion with attendees re: potential impact on educational practice and pedagogy (led by Penny Standen/Tom Hughes Roberts/Andrean Lazarov)
http://edurob.eu/
This project (543577-LLP-1-2013-1-UK-KA3-KA3MP) has been funded with support from the European Commission [Lifelong Learning Programme of the European Union]. This website reflects the views only of the author, and the European Commission cannot be held responsible for any use which may be made of the information contained therein.
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Educational Robotics for Students with disabilities (EDUROB) - brochure
http://edurob.eu/
This project (543577-LLP-1-2013-1-UK-KA3-KA3MP) has been funded with support from the European Commission [Lifelong Learning Programme of the European Union]. This website reflects the views only of the author, and the European Commission cannot be held responsible for any use which may be made of the information contained therein.
Can Computer-Assisted Training of Prerequisite Motor Skills Help Enable Communication in People with Autism? Data from a New Feasibility Study ( Matthew Belmonte, Emma Weisblatt, Alicia Rybicki, Beverley Cook, Caroline Langensiepen, David Brown, Manuj Dhariwal, Tanushree Saxena-Chandhok and Prathibha Karanth)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Game features of cognitive training (Michael P. Craven and Carlo Fabricatore)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
User involvement in design and application of virtual reality gamification to facilitate the use of hearing aids (Sue Cobb)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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Interactive Technologies and Games (ITAG) Conference 2016
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Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
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Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Me, My Game-Self, and Others: A Qualitative Exploration of the Game-Self (Nikolaos Kartsanis and Eva Murzyn)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
A comparison of humanoid and non-humanoid robots in supporting the learning of pupils with intellectual disabilities (Sarmad Aslam, PJ Standen, Nick Shopland and Andy Burton)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Keynote speaker - Fiorella Operto, ‘Robotics, A New Science’
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
Tell me what you want and I’ll show you what you can have: who drives design of technology for learning?
Associate Professor Sue Cobb
Interactive Technologies and Games (ITAG) Conference 2014
Health, Disability and Education
Dates: Thursday 16 October 2014 - Friday 17 October 2014
Location: The Council House, NG1 2DT, Nottingham, UK
Matthew Bates, Aoife Breheny, David Brown, Andy Burton and Penny Standen
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Interactive Technologies and Games (ITAG) Conference 2014
Health, Disability and Education
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Location: The Council House, NG1 2DT, Nottingham, UK
Urban Games: playful storytelling experiences for city dwellers
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Interactive Technologies and Games (ITAG) Conference 2014
Health, Disability and Education
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Interactive Technologies and Games (ITAG) Conference 2014
Health, Disability and Education
Dates: Thursday 16 October 2014 - Friday 17 October 2014
Location: The Council House, NG1 2DT, Nottingham, UK
Immersive Virtual Reality Simulation Deployment in a Lean Manufacturing Environment
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Interactive Technologies and Games (ITAG) Conference 2014
Health, Disability and Education
Dates: Thursday 16 October 2014 - Friday 17 October 2014
Location: The Council House, NG1 2DT, Nottingham, UK
From SnappyApp to Screens in the Wild: Gamifying an Attention Hyperactivity Deficit Disorder continuous performance test for public engagement and awareness
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Interactive Technologies and Games (ITAG) Conference 2014
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Enhancing the measurement of clinical outcomes using Microsoft Kinect choices (Philip Breedon, Bill Byrom, Luke Siena and Willie Muehlhausen)
1. Enhancing the Measurement of Clinical
Outcomes Using Microsoft Kinect
Philip Breedon and Francesco Luke Siena
Design for Health and Wellbeing
Research Group
Nottingham Trent University
Bill Byrom and Willie Muehlhausen
Product Innovation
ICON Clinical Research
2. 2
Presentation Overview
Overview of Clinical Trials
Motion Capture Platforms in Healthcare
Review of Kinect Applications for Outcomes Measurement
Example Measurement System
5. • Clinical trials rely upon robust and validated methodologies to measure health status and to
detect treatment-related changes in health status over time
• In some cases outcomes measures used rely on subjective ratings by the investigators at
each study research site.
– performance, balance, movement or mobility based on observation of the patient conducting a
specified movement or activity.
• Subjective ratings are not very sensitive to detecting small improvements
– Inter-rater reliability
• Objective measures preferred
– More sensitive
– Less prone to rater variability
– Able to measure detailed or subtle aspects of movement and mobility.
5
Objective measurement
7. 1. 3D Camera Systems and Sensors
2. Benefits of Motion Capture Platforms
3. Comparison Of Key Hardware & Utilities
4. Understanding The Progress Within The Motion Capture Platform Market
7
Motion Capture Platforms in Healthcare
9. 9
Motion Capture Platforms in Healthcare
• 3D camera systems and sensors have great potential to continue
having a positive impact on the market in a variety of industries,
especially within health care and clinical platforms.
• Hardware specification improvements may still be required when
considering accurate tracking of fine or rapid movements, and
therefore the sampling rates associated with the capture of this
data may need to improve.
• The application of motion capture camera systems and technology
in clinical and home health care applications, especially within the
rehabilitation sector is constantly evolving.
• Platforms such as Neuroforma, JINTRONIX, Stroke Recovery with
Kinect and Face To Face have recently been developed, amongst
others.
10. • There is a growing body of applications utilising motion capture
technology that study or encourage movement in wellness, healthcare
and clinical research.
• The area of rehabilitation is constantly exploring ways of providing
engaging environments through regular exercise regimes to enable
patient feedback and correction.
• Ensuring exercises are being performed correctly for optimal benefit.
• Enabling remote assessment and adjustment of exercise regimes
between clinic visits ensures regular patient contact and reviews which
can be monitored.
10
Benefits Of Motion Capture Platforms In Healthcare
11. 11
Comparison of Microsoft Kinect 1.0 & 2.0 For
HealthCare Utility Applications
Function Kinect 1.0 Kinect 2.0
RGB Camera (Pixel) 1280 × 1024 or 640 × 480 1920 × 1080
Depth Camera (Pixel) 640 × 480 512 × 424
Sampling Rate (FPS / Hz) 30 FPS 30 FPS
SDK 1.8 Compatibility Yes No
SDK 2.0 Compatibility No Yes
Face Tracking Yes Yes
Expression Recognition No (Possible With Additional Algorithms) Yes
Bone Orientations No Yes
Body Joint Forces No Yes
Hand Tracking No (Possible With Additional Tools) Yes
Muscle Simulation No Yes
Heart Rate Measurement No Yes
* Price & Specifications as of May 2016
12. Capability / Function Intel RealSense SR300 Kinect 2.0
RGB Camera (Pixel) 1080p at 30 FPS, 720p at 60 FPS 1920 × 1080 at 30 FPS
Depth Camera (Pixel) Up to 640 x 480 at 60 FPS (Fast
VGA, VGA), HVGA at 110 FPS
512 × 424 at 30 FPS
Skeletal Joint Definition Points 22 26
Face Tracking & Recognition Yes Yes
Expression Recognition Yes Yes
Gesture Recognition Yes Yes
Hand Tracking Yes Yes
Audio Stream Dual Array Microphones 4-Mic-Array
Connectivity (USB) 3.0 3.0
Approx. price (USD)* 130 190
12
Comparison of Intel® RealSense TM SR300 and
Microsoft Kinect 2.0 For HealthCare Systems
* Price & Specifications as of May 2016
13. 13
Why Champion The Intel® RealSense™ ?
• The Intel camera offers greater resolution and sampling rate
in comparison to Kinect 2.0, which may offer advantages
when tracking fine or fast movements.
• One of the novelties of the Intel RealSense 3D camera range
is its versatility for integration into a variety of platforms,
yet at the same time it remains affordable.
• Intel have developed a number of Intel RealSense camera
systems which can be integrated into a variety of platforms
whether this be Desktop PC’s, All-In-One PC’s, 2 In 1 PC’s,
external camera systems, smartphones and tablet kits and
even a robotics.
14. Review of Kinect Applications for Outcomes
Measurement
Bill Byrom, ICON
15. 1. Gait and balance
2. Upper extremity movement
3. Chest wall motion analysis
4. Facial analysis
15
Four main areas of measurement
16. • Various performance tests
proposed
– Short walking tests
– Treadmill walking tests
– Balance tests
• Spline interpolation to estimate
100 Hz sampling frequency
• Custom error correction
technique to improve data
artefact identification
16
Gait and balance
Pfister A. et al. (2014)
17. 17
Gait and balance
Ref Performance Measure Indication Comparator n Validation evidence
[1] Treadmill
walking tests
Hip/knee flexion/extension
Stride timing
Healthy
volunteers
(HV)
VICON motion
capture
28 Kinect underestimated flexion,
overestimated extension. Stride timing
often well correlated.
[2] Short walk Velocity, stride length,
hip/knee ROM
MS + HV PRO (MSWS)
ClinRO (EDSS)
20 Able to distinguish MS form controls
Reliability good except step width and hip
ROM
[3] 6 m walk Step length, foot swing
velocity, mean and peak
gait velocity, asymmetry
Stroke 10mWT, TUG,
Step test
30 Kinect parameters reliable: ICCs > 0.8
Feasible to instrument gait analysis
[4] Standing,
stepping, walk
on spot, UPDSS
Various PD + HV VICON motion
capture
19 Good for gross movements
Poor for fine movement
Good correlation with VICON (r > 0.8)
[5] Short max
speed walk
Speed; L/R, Up/Down and
3D deviation; speed
deviation
MS + HV 25 foot walk
test
44 Able to differentiate MS and controls
Good concordance with 25-foot walk test
18. 18
Upper extremity movement
Lin J-L. et al. (2014)
• Range of motion and
reaching volume
estimated from various
performance tests
– Standard range of motion
movements
– Movement task
19. Upper extremity movement
Ref Performance Measure Indication Comparator n Validation evidence
[6] Shoulder
movement
Shoulder flexion,
abduction, rotation
Adhesive
capsulitis + HV
Goniometer 27 ICCs: 0.864-0.942
[7] FMA / ARAT Shoulder/elbow/wrist
flexion, abduction, rotation
Stroke Impulse motion cap.
+ clinician ass.
9 MC: R2 = 0.64, p < 0.001
Clin. Ass: R2 = 0.86, p < 0.001
[8] Arm
movement
Shoulder flexion,
abduction, rotation,
extension
Healthy
volunteers (HV)
Goniometer 10 r = 0.86 to 0.99
[9] Arm
movement
3D workable reaching
space
HV Impulse motion
capture
10 R2 = 0.79
[10] Pediatric
Functional
Assessment
Index finger and thumb,
wrist, elbow, shoulder
ROM
HV Clinician assessment 12 “Technically sound approach”
[11] Movement
task
Involuntary movements /
dyskinesia
HV Clinician assessment 4 Cohen’s kappa 0.85, p < 0.05
[12] Fugl-Meyer,
WMFT, ARAT
Shoulder, elbow and wrist
position
HV Optitrack motion
capture
10 “Kinect is sufficiently accurate
and responsive”
[13] Arm/hand
movements
Machine learning
identification
MS Differentiate MS
from HV
1041 “Automated MS assessment
possible”
20. • Four Kinect cameras used to generate a 3D image
of the chest
• Performance test:
– Quiet breathing for 20 s, followed by a relaxed vital
capacity (VC) manoeuver (maximum inspiration and
expiration) and followed by 20 s of quiet breathing.
• Tidal volume, Respiratory Rate, and minute
ventilation compared to spirometry
– Good concordance for
• Cystic Fibrosis patients: r>0.8656
• Healthy volunteers r> 0.922
20
Chest wall analysis
21. 21
Facial analysis
Face to Face solution
• Rehabilitation system for facial paralysis in
stroke patients.
• Recognizes facial expressions
• Facial exercise performance is assessed by
the system and scored according to how
well the user can undertake each of the
defined set of expressions.
• Potential to apply to providing longitudinal
objective measures of change to assess
treatment effects.
22. 22
Summary findings
• May be less able to measure fine or rapid
movements
– Sampling rate of camera
– Resolution and depth of vision
• Joint detection accuracy with conventional SDK may
limit some applications
• May provide a low cost alternative to specialist labs
or subjective endpoints in large scale trials
24. • Objectives
• Understand how to develop
applications using the Kinect
Windows SDK
• Demonstrate the concept of health
outcomes measurement using Kinect
• Input into definition of future
requirements
24
Proof of concept: shoulder ROM
27. References
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3D motion capture for gait analysis . J Med Eng Technol; 38: 274-280.
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using Kinect camera. Technology and Health Care ; 21:641–656
[3] Clarke R.A. et al. (2015). Instrumenting gait assessment using the Kinect in
people living with stroke: reliability and association with balance tests. J
NeuroEngineering and Rehab; 12:15-23.
[10] Rammer J.R. et al. (2014). Evaluation of Upper Extremity Movement
Characteristics during Standardized Pediatric Functional Assessment with a
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Soc. 2014: 2525–2528.
[4] Galna B. et al. (2014). Accuracy of the Microsoft Kinect sensor for
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39: 1062–1068
[11] Li S.et al. (2015). Quantitative Assessment of ADL: A Pilot Study of Upper
Extremity Reaching Tasks. J Sensors; Article ID 236474.
[5] Behrens J. et al. (2014). Using perceptive computing in multiple sclerosis -
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[12] Webster D. et al. (2014). Experimental Evaluation of Microsoft Kinect’s
Accuracy and Capture Rate for Stroke Rehabilitation Applications. IEEE Haptics
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[6] Lee S.H. et al. (2015). Measurement of Shoulder Range of Motion
in Patients with Adhesive Capsulitis Using a Kinect. PLOS ONE10(6):
e0129398.
[13] Kontschieder P. et al. (). Quantifying Progression of Multiple Sclerosis via
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Computing and Computer-Assisted Intervention – MICCAI 2014, Volume 8674
of the series Lecture Notes in Computer Science); pp 429-437.
[7] Olesh E.V. (2014). Automated Assessment of Upper Extremity Movement
Impairment due to Stroke. PLoS ONE 9(8): e104487
[14] Harte J.M. et al. (2015). Chest wall motion analysis in healthy volunteers and
adults with cystic fibrosis using a novel Kinect-based motion tracking system.
Med. Biol. Eng. Comput.; DOI 10.1007/s11517-015-1433-1.
[15] Breedon P. et al. (2014). First for Stroke: using the Microsoft' Kinect' as a facial
paralysis stroke rehabilitation tool. Int J Integrated Care (IJIC), 14.