Falls and injuries in older people are associated with specific gait parameters.  PhD student, Nethra Ganesh, is developing a real-world gait parameter assessment using body worn sensors.  This enables physical activity and walking episodes to be measured outside a laboratory, with a view to preventing falls.   Nethra presented her phase one findings recently at the 2017 International Society of Posture and Gait Research World Congress in Ft. Lauderdale, USA.  Her poster was titled: Validation of walking episode and physical activity detection in supervised and free living conditions using triaxial accelerometers.

In addition to presenting her research, Nethra won a competition at the ISPGR World Congress 2017 Symposium.  Participants were informed that an activity had been recorded from a short unseen video (approx. 2 minutes duration).  Participants were provided with the triaxial accelerometer raw signal data from the recorded event and asked to analyse these signals and predict what activity/event was actually recorded in the unseen video.  Nethra came first in the competition correctly identifying the sequence of activities as a walking episode followed by a backward fall and rest phase.

The next step in Nethra’s research process is to develop a robust algorithm that can extract and validate these spatio-temporal parameters that can help in prediction and assessment of falls among frail older people.