Deepmotion: a deep convolutional neural network on inertial body sensors for gait assessment in multiple sclerosis

Gong, Minglun; Goldman,; Lach,

doi:10.1109/wh.2016.7764572

Cited by 22 publications

(17 citation statements)

References 24 publications

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“…Several factors like muscle power loss, level of spasticity, degree of instability due to impaired coordination and degree of sensory impairment have been identified to induce gait variability in PwMS [23]. Several studies have been conducted to monitor and assess gait deterioration [24][25][26][27]. In a study by Kaufman et al [28], the Timed 25-foot walk was used to assess gait deterioration and it is reported that an increase of 20% in the walking time between two trials signifies a clinically-relevant deterioration of gait.…”

Section: Tests For Gait and Balance Assessmentmentioning

confidence: 99%

“…Following a computational approach, Gong, Goldman and Lach [26] proposed a Deep Convolutional Neural Network (DCNN) to learn the temporal and spectral associations among the time-series motion data captured by inertial body sensors. A simulated model was developed to train the CNN, and then the trained model assessed gait performance in a pilot dataset with 41 subjects, 28 PwMS who had an EDSS score <4.5 and 13 healthy controls.…”

Section: Multimodal Gait and Balance Monitoringmentioning

confidence: 99%

“…Based on the studies presented above, it is evident that an inertial sensor-based setup is most commonly used in several studies [24,26,27,49,50,52] and measurements from these sensors have also been proven to be reliable to categorize patients to a specific disease state and differentiate an MS patient from a healthy subject [52]. An inertial sensor like the BioStamp sensor or the Xsens sensor usually consists of a 3-dimensional gyroscope and tri-axial accelerometer sensors.…”

Section: Multimodal Gait and Balance Monitoringmentioning

confidence: 99%

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A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

Rajavenkatanarayanan

Kanal

Tsiakas

et al. 2019

MTI

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Multiple sclerosis (MS) is a disease that affects the central nervous system, which consists of the brain and spinal cord. Although this condition cannot be cured, proper treatment of persons with MS (PwMS) can help control and manage the relapses of several symptoms. In this survey article, we focus on the different technologies used for the assessment and rehabilitation of motor impairments for PwMS. We discuss sensor-based and robot-based solutions for monitoring, assessment and rehabilitation. Among MS symptoms, fatigue is one of the most disabling features, since PwMS may need to put significantly more intense effort toward achieving simple everyday tasks. While fatigue is a common symptom across several neurological chronic diseases, it remains poorly understood for various reasons, including subjectivity and variability among individuals. To this end, we also investigate recent methods for fatigue detection and monitoring. The result of this survey will provide both clinicians and researchers with valuable information on assessment and rehabilitation technologies for PwMS, as well as providing insights regarding fatigue and its effect on performance in daily activities for PwMS.

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Section: Tests For Gait and Balance Assessmentmentioning

confidence: 99%

Section: Multimodal Gait and Balance Monitoringmentioning

confidence: 99%

Section: Multimodal Gait and Balance Monitoringmentioning

confidence: 99%

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A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

Rajavenkatanarayanan

Kanal

Tsiakas

et al. 2019

MTI

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“…However, these deep learning based systems are focused on recognition of gait patterns rather than estimation of walking speed. Gong et al proposed a DCNN to perform gait assessment for multiple sclerosis patients based on the spectral and temporal associations among sensor data collected with a number of inertial body sensors [16]. Gadaleta and Rossi adopted the DCNN to recognize a target user based on the way of their walking utilizing the accelerometer and gyroscope data of smartphone [17].…”

Section: Related Workmentioning

confidence: 99%

DeepWalking: Enabling Smartphone-Based Walking Speed Estimation Using Deep Learning

Shrestha

Won

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Walking speed estimation is an essential component of mobile apps in various fields such as fitness, transportation, navigation, and health-care. Most existing solutions are focused on specialized medical applications that utilize body-worn motion sensors. These approaches do not serve effectively the general use case of numerous apps where the user holding a smartphone tries to find his or her walking speed solely based on smartphone sensors. However, existing smartphone-based approaches fail to provide acceptable precision for walking speed estimation. This leads to a question: is it possible to achieve comparable speed estimation accuracy using a smartphone over wearable sensor based obtrusive solutions?We find the answer from advanced neural networks. In this paper, we present DeepWalking, the first deep learningbased walking speed estimation scheme for smartphone. A deep convolutional neural network (DCNN) is applied to automatically identify and extract the most effective features from the accelerometer and gyroscope data of smartphone and to train the network model for accurate speed estimation. Experiments are performed with 10 participants using a treadmill. The average root-mean-squared-error (RMSE) of estimated walking speed is 0.16m/s which is comparable to the results obtained by stateof-the-art approaches based on a number of body-worn sensors (i.e., RMSE of 0.11m/s). The results indicate that a smartphone can be a strong tool for walking speed estimation if the sensor data are effectively calibrated and supported by advanced deep learning techniques.

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“…), machine learning does not require a dynamic process model but sufficient data, including input data and output data of a specific system, hence a class of machine learning algorithms can be considered as datadriven modelling methods that are able to capture static or dynamic process behaviour in areas such as manufacturing and biomedical systems among others. Gong et al introduced a way to analysis time series signals and to create a human body model using CNNs [4]. Segreto et al evaluated the correlation between wavelet processed time series signals and the machining conditions using neural networks [5].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning: Convolutional Neural Networks with RBF Fuzzy Logic Classification Rules

Panoutsos

2018

2018 International Conference on Intelligent Systems (IS)

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Deepmotion: a deep convolutional neural network on inertial body sensors for gait assessment in multiple sclerosis

Cited by 22 publications

References 24 publications

A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

A Survey of Assistive Technologies for Assessment and Rehabilitation of Motor Impairments in Multiple Sclerosis

DeepWalking: Enabling Smartphone-Based Walking Speed Estimation Using Deep Learning

Interpretable Machine Learning: Convolutional Neural Networks with RBF Fuzzy Logic Classification Rules

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