SSA-LSTM-based locomotion mode recognition algorithm for the control of powered hip disarticulation prostheses

•Motion-pattern recognition for hip prostheses is investigated.•SSA-LSTM algorithm stabilises and refines single-hip-gait-pattern classification.•Dataset validation achieves over 99 % accuracy for healthy subjects and 96.4 % for hip amputees. Accurate recognition of locomotion modes is essential for...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Biomedical signal processing and control Ročník 112; s. 108583
Hlavní autoři:	Meng, Qiaoling, Sun, Zhenkun, Zhao, Jing, Castelli, Vincenzo Parenti, Yu, Hongliu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.02.2026
Témata:	Hip amputation Locomotion mode recognition Long short-term memory (LSTM) Lower limb prosthesis Sparrow search algorithm (SSA) Sparrow search algorithm (SSA) Hip amputation Long short-term memory (LSTM) Locomotion mode recognition Lower limb prosthesis
ISSN:	1746-8094
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	•Motion-pattern recognition for hip prostheses is investigated.•SSA-LSTM algorithm stabilises and refines single-hip-gait-pattern classification.•Dataset validation achieves over 99 % accuracy for healthy subjects and 96.4 % for hip amputees. Accurate recognition of locomotion modes is essential for the effective control of lower limb prosthetics, enabling amputees to navigate various terrains with ease. Despite advancements, current prosthetics lack adaptive capabilities for complex movements, necessitating intelligent systems that can discern user intentions from sensory inputs. This paper introduces the SSA-LSTM algorithm, which integrates the Sparrow Search Algorithm (SSA) with Long Short-Term Memory (LSTM) networks to enhance the stability and accuracy of motion pattern recognition in powered hip disarticulation prostheses. A comprehensive dataset was constructed, capturing gait characteristics of both healthy individuals and amputees across various motion modes, including level walking, stair climbing, and ramp navigation. The SSA-LSTM algorithm optimizes the LSTM’s initial state, thereby improving convergence and learning efficiency. Its performance was bench-marked against established methods, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), ensemble learning, and LSTM. The SSA-LSTM model achieved superior recognition accuracy, averaging over 99 % for healthy subjects and 96.4 % for hip disarticulation amputees. This model demonstrated faster convergence, underscoring the SSA’s role in enhancing the LSTM’s learning capabilities. The SSA-LSTM model, through its integration of SSA optimization, represents a significant advancement in locomotion mode recognition. This research contributes to the development of intelligent prosthetics by providing a more precise and responsive control mechanism, which is crucial for enhancing the mobility and independence of amputees.
ISSN:	1746-8094
DOI:	10.1016/j.bspc.2025.108583