Monitoring of Activity Levels of the Elderly in Home and Community Environments using Off the Shelf Cellular Handsets Initial Presentation by Martin Newell

Overview The Project The Project Testing Existing Application Testing Existing Application Analysing Results Analysing Results Determine Application Performance and Robustness on Multiple Handsets Determine Application Performance and Robustness on Multiple Handsets Additional Features Additional Features Application Issues Application Issues

The Project The purpose of this project is to use off the shelf mobile handsets to monitor and record the activity levels of elderly patients The purpose of this project is to use off the shelf mobile handsets to monitor and record the activity levels of elderly patients The handset implements a Gait Detection algorithm which uses an Average Magnitude Difference Function algorithm to determine when activity is being undertaken by the subject, and also to estimate the activity rate The handset implements a Gait Detection algorithm which uses an Average Magnitude Difference Function algorithm to determine when activity is being undertaken by the subject, and also to estimate the activity rate

Software calculates an AMDF every 3 seconds and determines weather there is clear periodicity in the motion of the subject which could be indicative of activity Software calculates an AMDF every 3 seconds and determines weather there is clear periodicity in the motion of the subject which could be indicative of activity Once this occurs the Application transmits an update to a server using the 3G interface on the handset Once this occurs the Application transmits an update to a server using the 3G interface on the handset Updated information includes - estimated activity rate, GPS information i.e. altitude and the handsets IMEI (international mobile equipment identity) Updated information includes - estimated activity rate, GPS information i.e. altitude and the handsets IMEI (international mobile equipment identity)

Gait Analysis and AMDF Terms Gait Analysis – This is the systematic study of human walking using the eye and brain of observers, augmented by instrumentation, for measuring body movement, body mechanics and the activity of muscles. Gait Analysis – This is the systematic study of human walking using the eye and brain of observers, augmented by instrumentation, for measuring body movement, body mechanics and the activity of muscles. Average Magnitude Difference Function - Average Magnitude Difference Function -

Testing Existing Application The existing application will be tested on a number of handsets to analyse its performance The existing application will be tested on a number of handsets to analyse its performance Test subjects will be given a handset with the application installed and asked to perform a number of tasks Test subjects will be given a handset with the application installed and asked to perform a number of tasks Tasks will include - walking at a brisk pace (this will be set by a metronome to help verify results) for seconds, walking up / down a slope, walking up / down a stairs Tasks will include - walking at a brisk pace (this will be set by a metronome to help verify results) for seconds, walking up / down a slope, walking up / down a stairs

Analysing Results When the application has been tested the results must be analysed to determine its performance and accuracy When the application has been tested the results must be analysed to determine its performance and accuracy Activity including ‘normal’ phone use (i.e. making / receiving calls and sms) is to be disregarded from results Activity including ‘normal’ phone use (i.e. making / receiving calls and sms) is to be disregarded from results Angle at which the subject is walking up and down the slope must also be calculated Angle at which the subject is walking up and down the slope must also be calculated

Applications Performance The applications performance must be determined from the results of the test The applications performance must be determined from the results of the test When the subject is carrying out the tasks the data from the accelerometer should show this When the subject is carrying out the tasks the data from the accelerometer should show this When the subject is resting the data from the accelerometer should show this also When the subject is resting the data from the accelerometer should show this also Readings from multiple handsets will be taken to verify performance also Readings from multiple handsets will be taken to verify performance also

Additional Features May include – Long Term Gait Pattern Monitoring May include – Long Term Gait Pattern Monitoring Automated Fall Detection Automated Fall Detection Automated Exercise and Energy Expenditure Estimation Automated Exercise and Energy Expenditure Estimation

Application Issues The main issue that needs to be addressed is the applications power consumption The main issue that needs to be addressed is the applications power consumption GPS functionality is the biggest consumer of power in this application GPS functionality is the biggest consumer of power in this application

Conclusions The developed system provides a non intrusive and potentially easily accepted methodology to monitor and analyse an elderly patient’s daily activity characteristics The developed system provides a non intrusive and potentially easily accepted methodology to monitor and analyse an elderly patient’s daily activity characteristics It would eliminate relying on the patient to fill a diary of their daily activity (which may not always be accurate) It would eliminate relying on the patient to fill a diary of their daily activity (which may not always be accurate)

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