Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Procházka, Aleš; Dostál, Otto; Cejnar, Pavel; Mohamed, Hagar Ibrahim; Pavelek, Zbyšek; Vališ, Martin; Vyšata, Oldřich

doi:10.1109/tnsre.2021.3051093

Cited by 30 publications

(23 citation statements)

References 52 publications

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“…All of the datasets were acquired during 26 cycling experiments in a hilly area on a route that is 12 km long, with an altitude difference of 300 m. Records were subsequently stored to the Garmin Connect website, exported as TCX files, converted to CSV files, and then imported to the MATLAB software for further processing. Accelerometric data were recorded by the mobile phone in the spine position, which was selected following previous studies [25], [41], which described the higher discriminative abilities of sensors located in the upper half of the body [42], [43] in comparison to other positions. The sampling frequency of the Android mobile phone sensor was 100 Hz during all cycling routes.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…Separate accelerometers or their synchronized systems are used in many different applications, which employ standard or deep learning methods for classification of motion patterns. For example, these methods are used in the diagnosis of motion disorders in neurology and ataxic gait monitoring [25], [26]. Analysis of the sensor placement for the best separation ability is one of the fundamental problems of these studies.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

et al. 2021

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Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well.INDEX TERMS Multimodal signal analysis, computational intelligence, machine learning, motion monitoring, accelerometer-derived cycling data, classification.

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Section: A Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

et al. 2021

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“…The classification results obtained after the selected number of training epochs were then analyzed by the receiver operating characteristic (ROC) [29,30]. The learning process evaluated the number of true/false negatives (TN, FN) and true/false positives (TP, FP) both in the negative set (class A: healthy tissue) and positive set (class B: caries).…”

Section: (A) (B)mentioning

confidence: 99%

Incremental deep learning for reflectivity data recognition in stomatology

Procházka

Charvát

Vyšata

et al. 2022

Neural Comput & Applic

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The recognition of stomatological disorders and the classification of dental caries are important areas of biomedicine that can hugely benefit from machine learning tools for the construction of relevant mathematical models. This paper explores the possibility of using reflectivity data to distinguish between healthy tissues and caries by deep learning and multilayer convolutional neural networks. The experimental data set includes more than 700 observations recorded in the stomatology laboratory. For rigor, the results obtained from the deep learning systems are compared with those evaluated for selected sets of features estimated for each observation and classified by a decision tree, support vector machine (SVM), k-nearest neighbor, Bayesian methods, and two-layer neural networks. The classification accuracy obtained for the deep learning systems was 98.1% and 94.4% for data in the signal and spectral domains, respectively, in comparison with an accuracy of 97.2% and 87.2% evaluated by the SVM method. The proposed method conclusively demonstrates how the artificial intelligence and deep learning methodology can contribute to improved diagnosis of dental problem in stomatology.

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“…In the context of movement-related diseases, ML/DL techniques have been used, together with data provided by wearable or vision-based sensors, to support gait assessment with the aim of diagnosis and/or evaluation of disease progression [10,11,[45][46][47][48][49][50][51][52][53][54][55][56][57]. The main focus of most contributions is the detection of abnormal gait based on information extracted from gait data obtained with accelerometers, gyroscopes and/or pressure sensors [45,46,56,57], or with RGB-D cameras [10,[47][48][49][50][51][52][53][54][55].…”

Section: Related Workmentioning

confidence: 99%

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning

Vilas-Boas

Rocha

Cardoso

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.

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Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Cited by 30 publications

References 52 publications

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring

Incremental deep learning for reflectivity data recognition in stomatology

Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning

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