Authors:
Carolina Amante
1
;
Catarina Consolado
1
;
Gabriel Pires
2
;
Ana Rita Londral
1
and
Cláudia Quaresma
1
Affiliations:
1
Physics Department, NOVA School of Science and Technology, 2892-516 Caparica, Portugal
;
2
Engineering Department, Polytechnic Institute of Tomar, 2300-313, Tomar, Portugal
Keyword(s):
Electromyography (EMG), Human–Computer Interfaces, Wearable Assistive Technologies, Amyotrophic Lateral Sclerosis, Deep Learning, Threshold, Wavelet, Online Signal Processing.
Abstract:
Motor neuron diseases, particularly amyotrophic lateral sclerosis (ALS), progressively impair motor control, limiting communication and autonomy and reinforcing the need for assistive technologies capable of operating reliably as function declines. These interfaces can also support health data collection that is relevant for disease monitoring, in particular, the extent of the neurodegenerative process. This work introduces a modular framework for developing and refining electromyography (EMG)-based human–computer interfaces (HCIs), centred on muscular contraction detection. Using EMG data from 64 healthy participants and 11 persons with ALS, the framework applies a Modular Analysis Approach and Design Space Exploration to assess detection strategies under ALS-specific signal heterogeneity and limited data availability, aiming for adaptability and generalisation. Three methods were examined: an amplitude-based threshold, an energy-based threshold using wavelet-domain multi-band decom
position, and a deep learning model combining convolutional neural networks and long short-term memory layers. Results showed that standard signal conditioning stabilized EMG signals but slightly reduced model performance. Incorporating ALS data improved generalisation to pathological signals and a median-based energy threshold enables dynamic, user-adaptive calibration. Multi-band energy combination did not yield meaningful improvements, indicating that total-band energy already captures the most relevant activity information. This framework offers a reproducible basis for testing and integrating detection modules and supports future exploration of multimodal or threshold-free learning-based detection strategies for wearable assistive technologies.
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