Amputee Gait Phase Recognition Using Multiple GMM-HMM
Gait analysis helps clinical assessment and achieves comfortable prosthetic designs for lower limb amputees, in which accurate gait phase recognition is a key component. However, gait phase detection remains a challenge due to the individual nature of prosthetic sockets and limbs. For the first time...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10795137/ |
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| Summary: | Gait analysis helps clinical assessment and achieves comfortable prosthetic designs for lower limb amputees, in which accurate gait phase recognition is a key component. However, gait phase detection remains a challenge due to the individual nature of prosthetic sockets and limbs. For the first time, we present a gait phase recognition approach for transfemoral amputees based on intra-socket pressure measurement. We proposed a multiple GMM-HMM (Hidden Markov Model with Gaussian Mixture Model emissions) method to label the gait events during walking. For each of the gait phases in the gait cycle, a separate GMM-HMM model is trained from the collected pressure data. We use gait phase recognition accuracy as a primary metric. The evaluation of six human subjects during walking shows a high accuracy of over 99% for single-subject, around 97.4% for multiple-subject, and up to 84.5% for unseen-subject scenarios. We compare our approach with the widely used CHMM (Continuous HMM) and LSTM (Long Short-term Memory) based methods, demonstrating better recognition accuracy performance across all scenarios. |
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| ISSN: | 2169-3536 |