Gait and activity recognition using commercial phones

• This paper implements a full gait and activity recognition system on a mobile device.
• A new distance metric (Cross Dynamic Time Warping Metric) is introduced.
• Single cycle classification and classification of full walks via majority voting.

This paper presents the results of applying gait and activity recognition on a commercially available mobile smartphone, where both data collection and real-time analysis was done on the phone. The collected data was also transferred to a computer for further analysis and comparison of various distance metrics and machine learning techniques. In our experiment 5 users created each 3 templates on the phone, where the templates were related to different walking speeds. The system was tested for correct identification of the user or the walking activity with 20 new users and with the 5 enrolled users. The activities are recognised correctly with an accuracy of over 99%. For gait recognition the phone learned the individual features of the 5 enrolled participants at the various walk speeds, enabling the phone to afterwards identify the current user. The new Cross Dynamic Time Warping (DTW) Metric gives the best performance for gait recognition where users are identified correctly in 89.3% of the cases and the false positive probability is as low as 1.4%.