  Fundamental knowledge in activity recognition of individuals with motor
disorders such as Parkinson's disease (PD) has been primarily limited to
detection 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 has
mainly relied on data from a large number of body locations which could
adversely affect user convenience and system performance. Here, individuals
with mild stages of PD and healthy subjects performed non-steady-state circuit
trials comprising stairs, ramp, and changes of direction. An offline analysis
using a linear discriminant analysis (LDA) classifier and a Long-Short Term
Memory (LSTM) neural network was performed for task recognition. The
performance of accelerographic and gyroscopic information from varied
lower/upper-body segments were tested across a set of user-independent and
user-dependent training paradigms. Comparing the F1 score of a given signal
across classifiers showed improved performance using LSTM compared to LDA.
Using LSTM, even a subset of information (e.g., feet data) in
subject-independent training appeared to provide F1 score > 0.8. However,
employing LDA was shown to be at the expense of being limited to using a
subject-dependent training and/or biomechanical data from multiple body
locations. The findings could inform a number of applications in the field of
healthcare monitoring and developing advanced lower-limb assistive devices by
providing insights into classification schemes capable of handling
non-steady-state and unstructured locomotion in individuals with mild
Parkinson's disease.
