Towards a context-based Bayesian recognition of transitions in locomotion activities

This paper presents a context-based approach for the recognition of transition between activities of daily living (ADLs) using wearable sensor data. A Bayesian method is implemented for the recognition of 7 ADLs with data from two wearable sensors attached to the lower limbs of subjects. A second Bayesian method recognises 12 transitions between the ADLs. The second recognition module uses both, data from wearable sensors and the activity recognised from the first Bayesian module. This approach analyses the next most probable transitions based on wearable sensor data and the context or current activity being performed by the subject. This work was validated using the ENABL3S Database composed of data collected from 7 ADLs and 12 transitions performed by participants walking on two circuits composed of flat surfaces, ascending and descending ramps and stairs. The recognition of activities achieved an accuracy of 98.3%. The recognition of transitions between ADLs achieved an accuracy of 98.8%, which improved the 95.3% accuracy obtained when the context or current activity is not considered for the recognition process. Overall, this work proposes an approach capable of recognising transitions between ADLs, which is required for the development of reliable wearable assistive robots.