An Evaluation of the Leap Motion Depth Sensing Camera for Tracking Hand and Fingers Motion in Physical Therapy by Darryl Charles, Katy Pedlow, Suzanne McDonough,
Ka Shek and Therese Charles
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This is a step-by-step instruction for controlled experiment design. I tried to simplify this complex and tedious process into a relatively simple and easy to follow recipe. This is the lecture slides I developed for CS3248: Design of Interaction Systems in the School of Computing, National University of Singapore
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This is a step-by-step instruction for controlled experiment design. I tried to simplify this complex and tedious process into a relatively simple and easy to follow recipe. This is the lecture slides I developed for CS3248: Design of Interaction Systems in the School of Computing, National University of Singapore
Introduction to Usability Testing for Survey ResearchCaroline Jarrett
The basics of how to incorporate usability testing in the development process of a survey. Workshp first presented at the SAPOR conference, Raleigh, North Carolina USA, October 2011 by Emily Geisen of RTI and Caroline Jarrett of Effortmark.
Modeling Electronic Health Records with Recurrent Neural NetworksJosh Patterson
Time series data is increasingly ubiquitous. This trend is especially obvious in health and wellness, with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. In 2009, intensive care units in the United States treated nearly 55,000 patients per day, generating digital-health databases containing millions of individual measurements, most of those forming time series. In the first quarter of 2015 alone, over 11 million health-related wearables were shipped by vendors. Recording hundreds of measurements per day per user, these devices are fueling a health time series data explosion. As a result, we will need ever more sophisticated tools to unlock the true value of this data to improve the lives of patients worldwide.
Deep learning, specifically with recurrent neural networks (RNNs), has emerged as a central tool in a variety of complex temporal-modeling problems, such as speech recognition. However, RNNs are also among the most challenging models to work with, particularly outside the domains where they are widely applied. Josh Patterson, David Kale, and Zachary Lipton bring the open source deep learning library DL4J to bear on the challenge of analyzing clinical time series using RNNs. DL4J provides a reliable, efficient implementation of many deep learning models embedded within an enterprise-ready open source data ecosystem (e.g., Hadoop and Spark), making it well suited to complex clinical data. Josh, David, and Zachary offer an overview of deep learning and RNNs and explain how they are implemented in DL4J. They then demonstrate a workflow example that uses a pipeline based on DL4J and Canova to prepare publicly available clinical data from PhysioNet and apply the DL4J RNN.
A systematic approach towards designing low-cost motor and cognitive rehabili...Sergi Bermudez i Badia
Nowadays it is widely accepted that games, and entertainment technologies in general, have very interesting features that, if used properly, can largely contribute to the effectiveness of treatments in different health domains. These games, also known as games-with-a-purpose, need to achieve a very difficult and interesting balance among science, health, engineering and entertainment. In this talk I will present the approach we follow at the NeuroRehabLab, where we combine games, Human Computer Interaction and clinical rehabilitation guidelines to develop interactive systems that are novel and effective tools for motor and cognitive rehabilitation, with special emphasis on stroke. I will discuss the effect of interface technology in motor-cognitive interference in task performance; a participatory design approach with health professionals to develop parameterized models for the training of Activities of Daily Living in a simulated environment; and how we automate the parameter selection process in these games by means of an adaptive approach. This strategy allows these systems to be used by patients of different cognitive and motor skills while still providing a personalized training.
Computer games for user engagement in Attention Deficit Hyperactivity Disorde...Karel Van Isacker
Computer games for user engagement in Attention Deficit Hyperactivity Disorder (ADHD) monitoring and therapy (Michael P. Craven and Maddie Groom)
Interactive Technologies and Games (ITAG) Conference 2015
Health, Disability and EducationDates: Thursday 22 October 2015 - Friday 23 October 2015 Location: The Council House, NG1 2DT
The three Finalists were:
*WEKIT — Wearable Experience for Knowledge Intensive Training — pitch by Paul Lefrere, Innovation Lead
*Sapien Labs (WINNER) — pitch by Tara Thiagarajan, Founder & Chief Scientist
*MyndYou — pitch by Shira Yama Nir, Project Manager
*Judged by: Bill Tucker, Senior Advisor to the K12 Education Program at the Bill & Melinda Gates Foundation; Eduardo Briceño, CEO and Co-founder of Mindset Works; John Cammack, Angel Investor; Neil Allison, Director of Business Model Innovation at Pearson North America
*Álvaro Fernández, CEO and Editor-in-Chief of SharpBrains
*Sarah Lenz Lock, Senior Vice President for Policy at AARP and Executive Director of the Global Council on Brain Health (GCBH)
*Dr. April Benasich, Director of the Baby Lab at the Rutgers Center for Molecular and Behavioral Neuroscience
*Chaired by: Dr. Cori Lathan, Co-Chair of the World Economic Forum’s Council on the Future of Human Enhancement
Slidedeck supporting session held during the 2017 SharpBrains Virtual Summit: Brain Health & Enhancement in the Digital Age (December 5-7th). Learn more at: https://sharpbrains.com/summit-2017/
Chapter 9: Evaluation techniques
from
Dix, Finlay, Abowd and Beale (2004).
Human-Computer Interaction, third edition.
Prentice Hall. ISBN 0-13-239864-8.
http://www.hcibook.com/e3/
We analyzed data that was recorded from a previous experiment done on the use of palm vein recognition that used two devices, cradle and non-cradle. Our goal was to see which device provided more benefits as a biometric system. We compared both devices by evaluating the data results that were provided . The main factors we looked at were capture times of each test subject when verifying using each device, and the amount of failure rates that occurred from both devices. From our conclusion, we believe the results we found will help lead to an improvement in the biometric system for future use. If the industry demands a clean environment, then non-cradle is recommended. If throughput time is of priority, then cradle is recommended.
Robotics and Education – EduRob Project Results Launch
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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)
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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.
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Introduction to Usability Testing for Survey ResearchCaroline Jarrett
The basics of how to incorporate usability testing in the development process of a survey. Workshp first presented at the SAPOR conference, Raleigh, North Carolina USA, October 2011 by Emily Geisen of RTI and Caroline Jarrett of Effortmark.
Modeling Electronic Health Records with Recurrent Neural NetworksJosh Patterson
Time series data is increasingly ubiquitous. This trend is especially obvious in health and wellness, with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. In 2009, intensive care units in the United States treated nearly 55,000 patients per day, generating digital-health databases containing millions of individual measurements, most of those forming time series. In the first quarter of 2015 alone, over 11 million health-related wearables were shipped by vendors. Recording hundreds of measurements per day per user, these devices are fueling a health time series data explosion. As a result, we will need ever more sophisticated tools to unlock the true value of this data to improve the lives of patients worldwide.
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A systematic approach towards designing low-cost motor and cognitive rehabili...Sergi Bermudez i Badia
Nowadays it is widely accepted that games, and entertainment technologies in general, have very interesting features that, if used properly, can largely contribute to the effectiveness of treatments in different health domains. These games, also known as games-with-a-purpose, need to achieve a very difficult and interesting balance among science, health, engineering and entertainment. In this talk I will present the approach we follow at the NeuroRehabLab, where we combine games, Human Computer Interaction and clinical rehabilitation guidelines to develop interactive systems that are novel and effective tools for motor and cognitive rehabilitation, with special emphasis on stroke. I will discuss the effect of interface technology in motor-cognitive interference in task performance; a participatory design approach with health professionals to develop parameterized models for the training of Activities of Daily Living in a simulated environment; and how we automate the parameter selection process in these games by means of an adaptive approach. This strategy allows these systems to be used by patients of different cognitive and motor skills while still providing a personalized training.
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Computer games for user engagement in Attention Deficit Hyperactivity Disorder (ADHD) monitoring and therapy (Michael P. Craven and Maddie Groom)
Interactive Technologies and Games (ITAG) Conference 2015
Health, Disability and EducationDates: Thursday 22 October 2015 - Friday 23 October 2015 Location: The Council House, NG1 2DT
The three Finalists were:
*WEKIT — Wearable Experience for Knowledge Intensive Training — pitch by Paul Lefrere, Innovation Lead
*Sapien Labs (WINNER) — pitch by Tara Thiagarajan, Founder & Chief Scientist
*MyndYou — pitch by Shira Yama Nir, Project Manager
*Judged by: Bill Tucker, Senior Advisor to the K12 Education Program at the Bill & Melinda Gates Foundation; Eduardo Briceño, CEO and Co-founder of Mindset Works; John Cammack, Angel Investor; Neil Allison, Director of Business Model Innovation at Pearson North America
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*Sarah Lenz Lock, Senior Vice President for Policy at AARP and Executive Director of the Global Council on Brain Health (GCBH)
*Dr. April Benasich, Director of the Baby Lab at the Rutgers Center for Molecular and Behavioral Neuroscience
*Chaired by: Dr. Cori Lathan, Co-Chair of the World Economic Forum’s Council on the Future of Human Enhancement
Slidedeck supporting session held during the 2017 SharpBrains Virtual Summit: Brain Health & Enhancement in the Digital Age (December 5-7th). Learn more at: https://sharpbrains.com/summit-2017/
Chapter 9: Evaluation techniques
from
Dix, Finlay, Abowd and Beale (2004).
Human-Computer Interaction, third edition.
Prentice Hall. ISBN 0-13-239864-8.
http://www.hcibook.com/e3/
We analyzed data that was recorded from a previous experiment done on the use of palm vein recognition that used two devices, cradle and non-cradle. Our goal was to see which device provided more benefits as a biometric system. We compared both devices by evaluating the data results that were provided . The main factors we looked at were capture times of each test subject when verifying using each device, and the amount of failure rates that occurred from both devices. From our conclusion, we believe the results we found will help lead to an improvement in the biometric system for future use. If the industry demands a clean environment, then non-cradle is recommended. If throughput time is of priority, then cradle is recommended.
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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.
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An Evaluation of the Leap Motion Depth Sensing Camera for Tracking Hand and Fingers Motion in Physical Therapy
1. Close Range Depth Sensing
Cameras for Virtual Reality based
Hand Rehabilitation
Darryl Charles1, Katy Pedlow2, Suzanne McDonough2, Ka Shek3, and Therese Charles3
1Computer
Science Research Institute, School of Computing and Information Engineering,
University of Ulster, Coleraine, Northern Ireland BT52 1SA
2Centre for Health and Rehabilitation Technologies, School of Health Sciences,
University of Ulster, Jordanstown, Northern Ireland BT37 0QB
3SilverFish Studios, Coleraine, Northern Ireland BT52 2NR
2. Introduction
• Our interest is in low cost, technology based,
games enhanced physical rehabilitation
• Previous work has used webcams and Kinect
– Effective but has issues, e.g. lag and resolution
• Leap Camera released
– Low lag, high resolution tracking of fingers
– We investigated beta camera and sdk
– Developed several VR therapies for fingers.
• In this paper we present results from trials with
professional physiotherapists
5. Leap Motion camera/controller
• Low cost (approx. £70 in the UK)
• Small (0.5” x 1.2” x 3” with a weight of 0.1
pounds)
• Built-in infrared LEDs to detect objects within
a dome of approximately 8 cubic feet above it
• Minimal latency and high spatial precision
(0.01mm)
7. Method
• Construct virtual simulations of 3 common rehab tasks
for hand and fingers
– Leap Motion and a natural interface controller
– Use a 3D game engine for construction
– Collaborate with commercial developers (SilverFish
Studios) and academics from the Centre for Health and
Rehabilitation Technologies (CHaRT)
• Trial simulated tasks with clinicians
– Give us understanding of the potential and limitations of
the Leap
– Obtain feedback from professionals at Regional Acquired
Brain Injury Unit (RABIU) at Musgrave Hospital in Belfast
before considering patient trials
8. Task 1 – Cotton Balls
Activity Description
Progression Parameters
Feedback Parameters
Cotton balls and a container are placed on a
table. The user is asked to pick the cotton balls
up off of the table and place them in the
container. The use is encouraged to use a pincer
grasp.
• Size of Container
• Distance of container from user
• Height of container
• Number of cotton balls
• Time required to place a set number of
cotton balls in the container
• Number of repetitions
9. Task 2 – Stacking Blocks
Activity Description
The user is given rectangular blocks
(wooden or plastic), and is asked to
build a tower by stacking blocks
vertically on the table.
Progression
• Distance of blocks from the user.
Parameters
• Number of blocks
• Size of blocks
Feedback Parameters • Number of repetitions in a set time
period
• Time: Time required to complete the
task
10. Task 3 - Nine Hole Peg Test (NHPT)
• The standardized equipment for the test typically
consists of:
– A board, in wood or plastic, with 9 holes (10 mm
diameter, 15 mm depth), placed apart by 32 mm
(Mathiowetz et al. 1985) or 50 mm (Heller et al. 1987)
– A container for the pegs. Initially the container was a
square box (100 x 100 x 10 mm) apart from the board.
The most current container is a shallow round dish at
the end of the board (Grice et al., 2003)
– 9 pegs (7 mm diameter, 32 mm length) (Mathiowetz
et al. 1985)
– Stopwatch
11. Virtual Tasks
• Video or live demo of software
Screenshots of three simulated rehab tasks. From left to right: Cotton Balls, Nine Hole Peg Test, and
Stacking Blocks.
12. Trial Participants
Participant
Q1. Occupation
1
Physiotherapist
2
3
4
5
6
7
8
Q2. Years of
Experience
10+
Q3. Use Games
for Rehab
No
Q4. Play Games
Occupational
Therapist
Student
10+
Yes
Occasionally
0
No
Never
Occupational
Therapist
Occupational
Therapist
Occupational
Therapist
Occupational
Therapist
Occupational
Therapist
1-2
Yes
Occasionally
3-5
Yes
Occasionally
3-5
Yes
Never
5-10
Yes
Occasionally
5-10
Yes
Never
Occasionally
13. Post Trial Questions
Background information
1. What is your occupation?
Physiotherapist / Occupational therapist / Other
2. How many years clinical experience do you have?
3. Do you use computer games for rehabilitation? YES/NO
System specific questions
(Answers on a Likert Scale 1 – 7)
4. Do you play games?
5. I feel that with practise I would become proficient in using the control interface
6. I feel that with practise I would become proficient in using the control interface
7. The tasks presented on the screen are easy to understand
8. The content on the screen is appropriate for the patient population
9. The prototypes provide a good illustration of all the functionalities I would require it to
have e.g. type of tasks, movements emphasised
Patient population related questions
(Answers on a Likert Scale 1 – 7)
10. I feel it would be easy to use this system in my clinical environment
11. I can see the benefit of this system for my general patient population
12. I can see the benefit of this system for the older patient population
13. I can see the benefit of this system for the younger patient population
14. I feel patients would be motivated to use this system
15. I feel my patients would benefit from this type of system in their home environment
16. I feel the system needs to be adapted to suit my patient population
Please state how it would need adapted (follow up comment)
16. Variation per Participant
Participant
Q1. Occupation
Q2. Years of
Experience
Q3. Use
Games for
Rehab
Q4. Play Games
Q5 – Q16
Mean
Response
Interquartile
Range
1
Physiotherapist
10+
No
Occasionally
1.33
0.75
2
Occupational
Therapist
10+
Yes
Occasionally
2.25
0.75
3
Student
0
No
Never
2.67
2.5
4
Occupational
Therapist
1-2
Yes
Occasionally
4.00
2
5
Occupational
Therapist
3-5
Yes
Occasionally
1.75
1.75
6
Occupational
Therapist
3-5
Yes
Never
4.17
1
7
Occupational
Therapist
5-10
Yes
Occasionally
1.00
0
8
Occupational
Therapist
5-10
Yes
Never
3.58
3
6 Yes
2 No
5 Occasionally
3 Never
2.59 (Mean)
1.47 (Mean)
Summary
17. Summary of Results
• Mean response to all questions = 2.59 (scale 1-7)
– Six of respondents had a mean response to the
questions of less than 3
• 4 clinicians over 5 years experience provided a
(mean = 2.04)
– 4 other responded with a (mean = 3.14)
•
•
•
•
Tasks easy to understand (mean = 1.88)
Good illustration of rehab tasks (mean = 2.38)
Suitable for young patients (mean = 1.6)
Suitable for home environment (mean = 2.5)
18. Discussion
• Ease of use of system
– We spent most time on this – it is tricky!
– Some clinicians did the interface tricky – especially at the start
• Issues: its an unfamiliar UI, hand position must remain parallel to table
surface (+/- 10 degrees or so)
• Lessons: user needs time to attune, lots of positions cues required, VR
headset like Oculus Rift may help
– Several clinicians felt that older people could have problems
learning to use the system
• Clinicians felt that the system has potential but the tasks
should be converted into games and could be more tailored
to their treatements
• Clinicians were excited about the use of the system in the
home – especially since it is so cheap and easy to set up.
19. Participant Comments
• “Develop programmes for different
orientation, i.e. vertical and horizontal”
(Participant 1)
• “More functional activities; i.e. lift a cup,
bringing to a mouth. Puzzles - to incorporate
cognitive skills” (Participant 4)
• “May be set games for different age groups”
(Participant 7)
20. Conclusion
• The results were very encouraging
• We have learned a lot about designed virtual
rehab. software that use the Leap controller
• Next phase
– Move software to Unity3D
– Design games for the Leap in collaboration with
clinicians
– Trial games with patients