Can the smartphone sensors be used for wheelchair accessibility studies? Dr. Manik Gupta Dr. Behzad Heravi Dr. Catherine Holloway Prof.
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1 Can the smartphone sensors be used for wheelchair accessibility studies? Dr. Manik Gupta Dr. Behzad Heravi Dr. Catherine Holloway Prof. Steve Hailes
2 Outline Research motivation Importance of wheelchair accessibility studies Bigger picture The ARCCS project Experiment details Conclusions
3 Motivation At present, there are of the order of 1 million people in wheelchairs in the UK and the number is growing as the population ages. Not just a UK problem- 80% of the world s 500 million disabled population live in developing countries
4 Technology solution Can technology be used make lives better for wheelchair users?? With the emergence of the IoT Sensor systems equipped with communications, data storage and machine learning algorithms can be deployed Used for data capture to automatically characterize environment and provide navigation information to wheelchair users
5 Choice of devices Off the shelf available physical activity monitoring devices eg. xsens Smartphone sensors eg. Onboard IMU and GPS sensors on android phones Bespoke sensor devices eg. Orisen Prime and Orisen Spectrum developed at CS, UCL.
6 Accessible Routes from Crowdsourced Cloud Services (ARCCS) Main idea Use a crowd-sourcing approach to design intelligent route planning system for wheelchair users in an urban environment Mobile phone app that ubiquitously collects IMU/GPS data using a user interactive platform and transmits anonymous data to the ARCCS cloud storage
7 ARCCS project Sensor devices mounted on wheelchair Multi-radio interface Bluetooth Data presentation Route planning Information processing Communications Security and privacy Cloud storage manager 3G Smartphone Zigbee, Cloud Environmental sensors Metadata capture Wheelmap.org, twitter External information about hazards from local authorities
8 Experiments Comparison between two different Android phones using off the shelf available app (Sensor kinetics pro) Comparison between Android phone using custom built app, Orisen Prime and Orisen Spectrum
9 Comparison between accelerometer data from two different Android phones Measurements taken on pavements outside PAMELA lab
10 Comparison between accelerometer data from two different Android phones Tri-axial accelerometer data for the upslope journey
11 Comparison between peak accelerations obtained from two different Android phones ^ Galaxy S4 O Sony Experia Differences are caused due to different sensor sensitivity and calibration levels
12 Comparison between accelerometer data from an Android (Galaxy S4) phone, Orisen Prime and Orisen Spectrum Accelerometer measurements show close proximity between phone and spectrum
13 Comparison between gyroscope data from an Android (Galaxy S4) phone, Orisen Prime and Orisen Spectrum Gyroscope measurements show closer proximity amongst all the three devices
14 Conclusions and Future work Performance of the smartphone sensors is in close correspondence to the bespoke sensor devices, on the contrary smartphones are much easier and convenient for the end user Future research challenges to be addressed: Wheelchair motion capture and orientation estimation GPS road side correction Surface detection and accessible route planning Low-complexity approach for real-time implementation in embedded systems
15 References 1. Sonenblum, S.E., et al., Validation of an accelerometer-based method to measure the use of manual wheelchairs. Medical engineering & physics, (6): p Ambur, V., et al., Classifying Wheelchair Propulsion Patterns with a Wrist Mounted Accelerometer. 3. Hiremath, S.V. and D. Ding. Evaluation of activity monitors to estimate energy expenditure in manual wheelchair users. in Engineering in Medicine and Biology Society, EMBC Annual International Conference of the IEEE IEEE. 4. Mourcou, Q., et al. Wegoto: A Smartphone-based approach to assess and improve accessibility for wheelchair users. in Engineering in Medicine and Biology Society (EMBC), th Annual International Conference of the IEEE IEEE. 5. Fu, J., et al. Capturing and analyzing wheelchair maneuvering patterns with mobile cloud computing. in Engineering in Medicine and Biology Society (EMBC), th Annual International Conference of the IEEE IEEE.
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