37 Citations (SciVal)


Human Locomotion Mode Recognition (LMR) has the potential to be used as a control
mechanism for lower-limb active prostheses. Active prostheses can assist and restore a more natural
gait for amputees, but as a medical device it must minimize user risks, such as falls and trips. As such,
any control system must have high accuracy and robustness, with a detailed understanding of
its internal operation. Long Short-Term Memory (LSTM) machine-learning networks can perform
LMR with high accuracy levels. However, the internal behavior during classification is unknown,
and they struggle to generalize when presented with novel users. The target problem addressed in
this paper is understanding the LSTM classification behavior for LMR. A dataset of six locomotive
activities (walking, stopped, stairs and ramps) from 22 non-amputee subjects is collected, capturing
both steady-state and transitions between activities in natural environments. Non-amputees are
used as a substitute for amputees to provide a larger dataset. The dataset is used to analyze the
internal behavior of a reduced complexity LSTM network. This analysis identifies that the model
primarily classifies activity type based on data around early stance. Evaluation of generalization
for unseen subjects reveals low sensitivity to hyper-parameters and over-fitting to individuals’ gait
traits. Investigating the differences between individual subjects showed that gait variations between
users primarily occur in early stance, potentially explaining the poor generalization. Adjustment of
hyper-parameters alone could not solve this, demonstrating the need for individual personalization
of models. The main achievements of the paper are (i) the better understanding of LSTM for LMR,
(ii) demonstration of its low sensitivity to learning hyper-parameters when evaluating novel user
generalization, and (iii) demonstration of the need for personalization of ML models to achieve
acceptable accuracy
Original languageEnglish
Article number1264
Number of pages23
Issue number4
Publication statusPublished - 10 Feb 2021


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