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Fundamental knowledge in activity recognition of individuals with motordisorders such as Parkinson's disease (PD) has been primarily limited todetection of steady-state/static tasks (sitting, standing, walking). To date,identification of non-steady-state locomotion on uneven terrains (stairs,ramps) has not received much attention. Furthermore, previous research hasmainly relied on data from a large number of body locations which couldadversely affect user convenience and system performance. Here, individualswith mild stages of PD and healthy subjects performed non-steady-state circuittrials comprising stairs, ramp, and changes of direction. An offline analysisusing a linear discriminant analysis (LDA) classifier and a Long-Short TermMemory (LSTM) neural network was performed for task recognition. Theperformance of accelerographic and gyroscopic information from variedlower/upper-body segments were tested across a set of user-independent anduser-dependent training paradigms. Comparing the F1 score of a given signalacross classifiers showed improved performance using LSTM compared to LDA.Using LSTM, even a subset of information (e.g., feet data) insubject-independent training appeared to provide F1 score > 0.8. However,employing LDA was shown to be at the expense of being limited to using asubject-dependent training and/or biomechanical data from multiple bodylocations. The findings could inform a number of applications in the field ofhealthcare monitoring and developing advanced lower-limb assistive devices byproviding insights into classification schemes capable of handlingnon-steady-state and unstructured locomotion in individuals with mildParkinson's disease.

An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson's Disease