Human lower-extremity movement classification based on biomechanical sensor data: Machine learning approach

Hacer Kuduz; Fırat Kaçar

Makale detayları

Human lower-extremity movement classification based on biomechanical sensor data: Machine learning approach

Yazarlar:

Hacer Kuduz | Fırat Kaçar

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Building Technology

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1378-1390

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329108

pISSN:

1304-7191

Anahtar kelimeler:

Özet:

 Wearable biomechanical sensor signals can be used to precisely recognize human lower ex-tremity movements based upon gait parameters such as walking speed, which is an increasing-ly important field with a significant role in biomedical studies. In this study, human walking patterns were classified using wearable biomechanical sensors and machine learning and time series analysis techniques. Accurate classification of level-ground gait patterns of IMU, digital goniometer GON and electromyography EMG sensor data is of great importance in in-forming physicians and medical device innovators working in this discipline. For this study, an open access dataset recorded from four unilaterally placed IMUs, three GONs and eleven EMG sensors in 22 subjects at different walking speeds was used. The sliding time window method was used to extract features in the first part of biomedical signal processing. Then, the effects of various window lengths and single or multiple sensor models on machine learning classification performance are compared. The results of this study showed that the QSVM classifier and IMU-based sensor with a window length of 1000 5s had the highest classifi-cation accuracy of 0.954 to classify human gait at different walking speeds based on the pro-posed method. In addition, it is seen that the classifiers have different classification accuracy for the seven sensor models used. QSVM has higher accuracy in gait recognition compared to WNN and ESKNN classifiers. In particular, the accuracy 0.961 in the experiment using the IMU and GON multiple sensor and QSVM classifier is the highest among other sensor combinations and classifiers. When QSVM classification and gait recognition were compared, the accuracies were found as IMU 0.954, GON 0.827 and EMG 0.735 sensor models, respectively. Then, in dual sensor combination models, the highest accuracy was achieved in IMU-GON 0.961, IMU-EMG 0.895 and GON-EMG 0.776 sensor models, respectively. Finally, the accuracy of the IMU-GON-EMG model, in which all three sensors are included, is 0.919. The findings of this study showed that IMU sensor models improved the classification performance in level-ground gait pattern recognition, and their use together with GON sensor models contributed positively to this performance. It has been found that EMG sensor models show lower classification performance compared to IMU sensor modelsg the necessary pre-cautions were beneficial in terms of protecting the health of the employees.

 Wearable biomechanical sensor signals can be used to precisely recognize human lower ex-tremity movements based upon gait parameters such as walking speed, which is an increasing-ly important field with a significant role in biomedical studies. In this study, human walking patterns were classified using wearable biomechanical sensors and machine learning and time series analysis techniques. Accurate classification of level-ground gait patterns of IMU, digital goniometer GON and electromyography EMG sensor data is of great importance in in-forming physicians and medical device innovators working in this discipline. For this study, an open access dataset recorded from four unilaterally placed IMUs, three GONs and eleven EMG sensors in 22 subjects at different walking speeds was used. The sliding time window method was used to extract features in the first part of biomedical signal processing. Then, the effects of various window lengths and single or multiple sensor models on machine learning classification performance are compared. The results of this study showed that the QSVM classifier and IMU-based sensor with a window length of 1000 5s had the highest classifi-cation accuracy of 0.954 to classify human gait at different walking speeds based on the pro-posed method. In addition, it is seen that the classifiers have different classification accuracy for the seven sensor models used. QSVM has higher accuracy in gait recognition compared to WNN and ESKNN classifiers. In particular, the accuracy 0.961 in the experiment using the IMU and GON multiple sensor and QSVM classifier is the highest among other sensor combinations and classifiers. When QSVM classification and gait recognition were compared, the accuracies were found as IMU 0.954, GON 0.827 and EMG 0.735 sensor models, respectively. Then, in dual sensor combination models, the highest accuracy was achieved in IMU-GON 0.961, IMU-EMG 0.895 and GON-EMG 0.776 sensor models, respectively. Finally, the accuracy of the IMU-GON-EMG model, in which all three sensors are included, is 0.919. The findings of this study showed that IMU sensor models improved the classification performance in level-ground gait pattern recognition, and their use together with GON sensor models contributed positively to this performance. It has been found that EMG sensor models show lower classification performance compared to IMU sensor modelsg the necessary pre-cautions were beneficial in terms of protecting the health of the employees.

 Wearable biomechanical sensor signals can be used to precisely recognize human lower ex-tremity movements based upon gait parameters such as walking speed, which is an increasing-ly important field with a significant role in biomedical studies. In this study, human walking patterns were classified using wearable biomechanical sensors and machine learning and time series analysis techniques. Accurate classification of level-ground gait patterns of IMU, digital goniometer GON and electromyography EMG sensor data is of great importance in in-forming physicians and medical device innovators working in this discipline. For this study, an open access dataset recorded from four unilaterally placed IMUs, three GONs and eleven EMG sensors in 22 subjects at different walking speeds was used. The sliding time window method was used to extract features in the first part of biomedical signal processing. Then, the effects of various window lengths and single or multiple sensor models on machine learning classification performance are compared. The results of this study showed that the QSVM classifier and IMU-based sensor with a window length of 1000 5s had the highest classifi-cation accuracy of 0.954 to classify human gait at different walking speeds based on the pro-posed method. In addition, it is seen that the classifiers have different classification accuracy for the seven sensor models used. QSVM has higher accuracy in gait recognition compared to WNN and ESKNN classifiers. In particular, the accuracy 0.961 in the experiment using the IMU and GON multiple sensor and QSVM classifier is the highest among other sensor combinations and classifiers. When QSVM classification and gait recognition were compared, the accuracies were found as IMU 0.954, GON 0.827 and EMG 0.735 sensor models, respectively. Then, in dual sensor combination models, the highest accuracy was achieved in IMU-GON 0.961, IMU-EMG 0.895 and GON-EMG 0.776 sensor models, respectively. Finally, the accuracy of the IMU-GON-EMG model, in which all three sensors are included, is 0.919. The findings of this study showed that IMU sensor models improved the classification performance in level-ground gait pattern recognition, and their use together with GON sensor models contributed positively to this performance. It has been found that EMG sensor models show lower classification performance compared to IMU sensor modelsg the necessary pre-cautions were beneficial in terms of protecting the health of the employees.

 Wearable biomechanical sensor signals can be used to precisely recognize human lower ex-tremity movements based upon gait parameters such as walking speed, which is an increasing-ly important field with a significant role in biomedical studies. In this study, human walking patterns were classified using wearable biomechanical sensors and machine learning and time series analysis techniques. Accurate classification of level-ground gait patterns of IMU, digital goniometer GON and electromyography EMG sensor data is of great importance in in-forming physicians and medical device innovators working in this discipline. For this study, an open access dataset recorded from four unilaterally placed IMUs, three GONs and eleven EMG sensors in 22 subjects at different walking speeds was used. The sliding time window method was used to extract features in the first part of biomedical signal processing. Then, the effects of various window lengths and single or multiple sensor models on machine learning classification performance are compared. The results of this study showed that the QSVM classifier and IMU-based sensor with a window length of 1000 5s had the highest classifi-cation accuracy of 0.954 to classify human gait at different walking speeds based on the pro-posed method. In addition, it is seen that the classifiers have different classification accuracy for the seven sensor models used. QSVM has higher accuracy in gait recognition compared to WNN and ESKNN classifiers. In particular, the accuracy 0.961 in the experiment using the IMU and GON multiple sensor and QSVM classifier is the highest among other sensor combinations and classifiers. When QSVM classification and gait recognition were compared, the accuracies were found as IMU 0.954, GON 0.827 and EMG 0.735 sensor models, respectively. Then, in dual sensor combination models, the highest accuracy was achieved in IMU-GON 0.961, IMU-EMG 0.895 and GON-EMG 0.776 sensor models, respectively. Finally, the accuracy of the IMU-GON-EMG model, in which all three sensors are included, is 0.919. The findings of this study showed that IMU sensor models improved the classification performance in level-ground gait pattern recognition, and their use together with GON sensor models contributed positively to this performance. It has been found that EMG sensor models show lower classification performance compared to IMU sensor modelsg the necessary pre-cautions were beneficial in terms of protecting the health of the employees.

Makale detayları

Human lower-extremity movement classification based on biomechanical sensor data: Machine learning approach

Yazarlar:

Hacer Kuduz | Fırat Kaçar

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Building Technology

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1378-1390

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329108

pISSN:

1304-7191

Biomechanics

Biomedical Signal Processing

Human Movement

Classification

Machine Learning; Wearable Sensor

Sayı

Makale detayları

Human lower-extremity movement classification based on biomechanical sensor data: Machine learning approach

Yazarlar: Hacer Kuduz | Fırat Kaçar

Yayıncı: Yildiz Technical University

Ana Başlıklar: Building Technology

Dergi: Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1378-1390

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001329108

pISSN: 1304-7191

Biomechanics

Biomedical Signal Processing

Human Movement

Classification

Machine Learning; Wearable Sensor

Sayı

Yazarlar:

Hacer Kuduz | Fırat Kaçar

Yayıncı:

Yildiz Technical University

Ana Başlıklar:

Building Technology

Dergi:

Sigma Journal of Engineering and Natural Sciences Vol.42 Issue 5, pp.1378-1390

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001329108

pISSN:

1304-7191