Cycling Segments Multimodal Analysis and Classification Using Neural Networks

Procházka, Aleš; Vaseghi, Saeed; Charvátová, Hana; Ťupa, Ondřej; Vyšata, Oldřich

doi:10.3390/app7060581

Cited by 17 publications

(10 citation statements)

References 33 publications

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“…The present paper is devoted to the analysis of motion features [17] based upon the analysis of the signals recorded by accelerometers and gyrometers [18] located inside wearable devices, such as mobile phones [19,20] and tablets. These sensors are often used for gait analysis [21][22][23] or for monitoring physical activities [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

et al. 2019

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Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use of microelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities.

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Section: Introductionmentioning

confidence: 99%

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

et al. 2019

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“…Furthermore, FBLP-SVR requires only one snapshot to function. Signals of the geo-positioning system (GPS) and multichannel human heart rate during a very long cycling episode are analyzed in [44] to determine the specific biomedical features, obtain a relation between the heart rate and slopes for downhill and uphill cycling, and the mean heart rate evolution on flat segments. The signals are pre-processed using low-pass finite impulse response (FIR) noise-filtering.…”

Section: Signal Processingmentioning

confidence: 99%

Complex and Hypercomplex-Valued Support Vector Machines: A Survey

Arana‐Daniel

2019

Applied Sciences

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In recent years, the field of complex, hypercomplex-valued and geometric Support Vector Machines (SVM) has undergone immense progress due to the compatibility of complex and hypercomplex number representations with analytic signals, as well as the power of description that geometric entities provide to object descriptors. Thus, several interesting applications can be developed using these types of data and algorithms, such as signal processing, pattern recognition, classification of electromagnetic signals, light, sonic/ultrasonic and quantum waves, chaos in the complex domain, phase and phase-sensitive signal processing and nonlinear filtering, frequency, time-frequency and spatiotemporal domain processing, quantum computation, robotics, control, time series prediction, and visual servoing, among others. This paper presents and discusses the importance, recent progress, prospective applications, and future directions of complex, hypercomplex-valued and geometric Support Vector Machines.

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“…The ataxic gait monitoring of patients with the multiple sclerosis forms a very important problem in this area. The recent rapid progress of sensor technology and wireless communication links allow the use of different microelectromechanical sensor units (MEMS), video, depth and thermal camera systems [9], [10], and wearable devices [11]- [15] for the associated motion analysis [16]- [19]. Specific mathematical methods are then used to process data in the time, frequency, or scale domains to perform human activity analysis.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

Procházka

Dostál

Cejnar

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positions with a sampling frequency of 60 Hz. The deep learning system uses all of the frequency components in a range of 0, 30 Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

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Cycling Segments Multimodal Analysis and Classification Using Neural Networks

Cited by 17 publications

References 33 publications

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Complex and Hypercomplex-Valued Support Vector Machines: A Survey

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring

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