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

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.