Gait impairment is among the most common and affecting symptoms of multiple sclerosis (MS), occurring in more than 90% of patients as the disease progresses. Conventional clinical tests, such as the timed 25-foot walk (T25FW), are not always able to capture the entire richness of gait impairment, especially in everyday settings. To overcome these shortcomings, this article introduces a new remote gait monitoring system based on wearable smart socks embedded with inertial sensors. The system continuously receives high-frequency motion data and, therefore, enables gait auto-recognition and can improve the classification of MS-associated gait impairment. An end-to-end pipeline for data processing was developed, which involves sensor fusion techniques, semantic gait modeling, and machine learning classification. The segmentation and characterization of gait are performed using the spectral analysis of accelerometer and gyroscope signals, with short-time Fourier transform (STFT)-based feature extraction to identify the periodicity and quality of gait. In addition, a deep learning (DL) approach based on the combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to discriminate walking patterns with high precision that help detect abnormalities related to MS. Experimental validation was carried out on a population of people with MS (PwMS) and healthy controls, with our model achieving an average accuracy of 97.10% and an area under the curve (AUC) of 0.99 for severe MS classification. The Internet of Wearable Things (IoWT) paradigm introduced here continuous data acquisition and integration with other wearable sensors and offers a noninvasive and scalable solution for continuous gait monitoring. The results highlight the potential of this approach to improve clinical examination, enable early detection of mobility decline, and support individualized rehabilitation planning. Future studies will explore the incorporation of transformer-based artificial intelligence (AI) models to further improve the classification of MS disability.
Gait impairment is among the most common and affecting symptoms of multiple sclerosis (MS), occurring in more than 90% of patients as the disease progresses. Conventional clinical tests, such as the timed 25-foot walk (T25FW), are not always able to capture the entire richness of gait impairment, especially in everyday settings. To overcome these shortcomings, this article introduces a new remote gait monitoring system based on wearable smart socks embedded with inertial sensors. The system continuously receives high-frequency motion data and, therefore, enables gait auto-recognition and can improve the classification of MS-associated gait impairment. An end-to-end pipeline for data processing was developed, which involves sensor fusion techniques, semantic gait modeling, and machine learning classification. The segmentation and characterization of gait are performed using the spectral analysis of accelerometer and gyroscope signals, with short-time Fourier transform (STFT)-based feature extraction to identify the periodicity and quality of gait. In addition, a deep learning (DL) approach based on the combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to discriminate walking patterns with high precision that help detect abnormalities related to MS. Experimental validation was carried out on a population of people with MS (PwMS) and healthy controls, with our model achieving an average accuracy of 97.10% and an area under the curve (AUC) of 0.99 for severe MS classification. The Internet of Wearable Things (IoWT) paradigm introduced here continuous data acquisition and integration with other wearable sensors and offers a noninvasive and scalable solution for continuous gait monitoring. The results highlight the potential of this approach to improve clinical examination, enable early detection of mobility decline, and support individualized rehabilitation planning. Future studies will explore the incorporation of transformer-based artificial intelligence (AI) models to further improve the classification of MS disability. Read More
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