Automatic recognition of movement patterns in the vojta-therapy using RGB-D data

Khan, Muhammad Hassan; Helsper, Jullien; Boukhers, Zeyd; Grzegorzek, Marcin

doi:10.1109/icip.2016.7532555

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…We evaluated the performance of our proposed features using different classification techniques. A short description of each of the technique is described in the following: The training process aims to find such a hyperplane that should be in the middle of positive and negative instances, and the distance of hyperplane with the nearest positive and negative instances should be maximized [3,74]. We used simple LibLinear SVM [73], and the optimal value or hyperparameter/regularization parameter C is selected empirically within the range of (2 −6 -2 6 ).…”

Section: Classificationmentioning

confidence: 99%

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad

Khan

Nisar

et al. 2021

Sensors

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Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

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Section: Classificationmentioning

confidence: 99%

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad

Khan

Nisar

et al. 2021

Sensors

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“…w,r − y t,e w ,r ) 2 (9) As shown in the above equation where and µ b t are covariance and mean matrix of the paradigm at time t and for w, w ∈ W and r, r ∈ T w . The Dynamic time warping (DTW) algorithm for aligning time series data with non-linear warping to minimize the spacing of the time series.…”

Section: F Gaussian Mixture Regression -Gaussian Mixture Model (Gmr-mentioning

confidence: 99%

Rehabilitation Exercise Recognition and Evaluation Based on Smart Sensors With Deep Learning Framework

Zhang¹,

Su²,

He³

2020

IEEE Access

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Exercise therapy is seen as one of the major treatments for the rehabilitation for patients, particularly using modern technologies, such as virtual reality or augmented reality. Computer-assisted physical rehabilitation training involves measuring performance by analyzing the movement data collected with a sensory system during prescribed rehabilitation exercises. Human activity recognition is a challenging topic for machine learning in the present area of research. Since the sensor-based activity recognition seeks deep knowledge from various low-level sensor readings concerning human activities. In this paper, the Smart Sensor-based Rehabilitation Exercise Recognition (SSRER) system has been proposed using a deep learning framework. For the recognition of rehabilitation exercise with sensor information, a convolutional neural network (CNN) has been used on dynamic platform(D-CNN) where it has sensory data for physical rehabilitation exercise body movement by Gaussian mixture models (GMM). The input signals and GMMs are in various segments contains shapes for many CNN routes. To retrieve the state transition likelihood of hidden states, the Sensor (S-CNN) utilizes the algorithm of improved lossless information compression as discriminant features of various movements. Therefore, the hybridized CNN of the Sensor (S-CNN) and D-CNN are combined with a deep learning classifier to assess every rehabilitation class exercise at different levels. The categorized deep learning methods show improved performance with best-learned features for any rehabilitation exercise. The difference between the best attribute and the test score analyzed mathematically with our collected data and a variety of activity recognition datasets has been illustrated in this article with test results. INDEX TERMS Deep learning model, convolutional neural network, rehabilitation exercise recognition, sensors. I. BACKGROUND AND INTRODUCTION OF REHABILITATION EXERCISE RECOGNITION

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“…A typical home-based rehabilitation exercise program (with no digital tools integrated) is based on a handbook of instructions and directions about the frequency, intensity, and correct performance of physiotherapy exercises [ 8 ]. Yet, such programs do not always ensure the full recovery of patients, as compliance rates are low [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Low patient motivation and adherence to the appropriate rehabilitation exercise programs have been reported, and these consequently prolong treatment times and impose higher health care costs [6]. While various factors have been identified that contribute to low compliance, lack of continuous feedback is an important factor, and accurate monitoring of patient exercises by medical professionals in a home environment is considered essential [7,8].…”

Section: Introductionmentioning

confidence: 99%

Automated Assessment of Balance Rehabilitation Exercises With a Data-Driven Scoring Model: Algorithm Development and Validation Study

Tsakanikas¹,

Gatsios²,

Pardalis³

et al. 2022

JMIR Rehabil Assist Technol

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Background Balance rehabilitation programs represent the most common treatments for balance disorders. Nonetheless, lack of resources and lack of highly expert physiotherapists are barriers for patients to undergo individualized rehabilitation sessions. Therefore, balance rehabilitation programs are often transferred to the home environment, with a considerable risk of the patient misperforming the exercises or failing to follow the program at all. Holobalance is a persuasive coaching system with the capacity to offer full-scale rehabilitation services at home. Holobalance involves several modules, from rehabilitation program management to augmented reality coach presentation. Objective The aim of this study was to design, implement, test, and evaluate a scoring model for the accurate assessment of balance rehabilitation exercises, based on data-driven techniques. Methods The data-driven scoring module is based on an extensive data set (approximately 1300 rehabilitation exercise sessions) collected during the Holobalance pilot study. It can be used as a training and testing data set for training machine learning (ML) models, which can infer the scoring components of all physical rehabilitation exercises. In that direction, for creating the data set, 2 independent experts monitored (in the clinic) 19 patients performing 1313 balance rehabilitation exercises and scored their performance based on a predefined scoring rubric. On the collected data, preprocessing, data cleansing, and normalization techniques were applied before deploying feature selection techniques. Finally, a wide set of ML algorithms, like random forests and neural networks, were used to identify the most suitable model for each scoring component. Results The results of the trained model improved the performance of the scoring module in terms of more accurate assessment of a performed exercise, when compared with a rule-based scoring model deployed at an early phase of the system (k-statistic value of 15.9% for sitting exercises, 20.8% for standing exercises, and 26.8% for walking exercises). Finally, the resulting performance of the model resembled the threshold of the interobserver variability, enabling trustworthy usage of the scoring module in the closed-loop chain of the Holobalance coaching system. Conclusions The proposed set of ML models can effectively score the balance rehabilitation exercises of the Holobalance system. The models had similar accuracy in terms of Cohen kappa analysis, with interobserver variability, enabling the scoring module to infer the score of an exercise based on the collected signals from sensing devices. More specifically, for sitting exercises, the scoring model had high classification accuracy, ranging from 0.86 to 0.90. Similarly, for standing exercises, the classification accuracy ranged from 0.85 to 0.92, while for walking exercises, it ranged from 0.81 to 0.90. Trial Registration ClinicalTrials.gov NCT04053829; https://clinicaltrials.gov/ct2/show/NCT04053829

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Automatic recognition of movement patterns in the vojta-therapy using RGB-D data

Cited by 14 publications

References 14 publications

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Rehabilitation Exercise Recognition and Evaluation Based on Smart Sensors With Deep Learning Framework

Automated Assessment of Balance Rehabilitation Exercises With a Data-Driven Scoring Model: Algorithm Development and Validation Study

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