Characterization of Upper Limb Functionality Caused by Neuromuscular Disorders using Novel Motion Features from a Specialized Gaming Platform

Chytas, Achilleas; Fotopoulos, Dimitris; Kilintzis, Vassilis; Koutsiana, Elisavet; Ladakis, Ioannis; Kiana, E.; Loizidis, Theodoros; Chouvarda, Ioanna

doi:10.5220/0010244400600068

Cited by 2 publications

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“…Of the 88 publications, 29 applied a filter to their data (Table 8), with 16 in the healthcare domain, eight in the sports domain, and five in the wellness domain. The most common filter was the Butterworth filter, which was used in 18 publications (16 lowpass [29], [30], [32], [52], [54], [68], [69], [73], [76], [86], [105], [107], [108], [109], [112], [113], one high-pass [84], and one band-pass [50]), and then the moving average used in five publications [46], [92], [93], [94], [95]. Filters are most commonly applied to data captured from inertial sensors, with 14/39 publications that used inertial sensors applying them [29], [32], [50], [52], [68], [69], [73], [76], [102], [103], [109], [112], [113], [119].…”

Section: ) Preprocessingmentioning

confidence: 99%

“…Geometric features provide information about the relationship between joints in space using linear algebra operations (e.g., angles and distances). These features were used in 21 publications [34], [45], [46], [54], [59], [60], [61], [62], [68], [76], [84], [85], [89], [90], [92], [93], [94], [95], [99], [111], [118]. Kinematic features, which were used in 20 publications, describe the motion of joints [21], [32], [45], [54], [58], [59], [60], [61], [62], [66], [69], [84], [85], [86], [87], [91], [92], [93], [94], [95].…”

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

“…These features were used in 21 publications [34], [45], [46], [54], [59], [60], [61], [62], [68], [76], [84], [85], [89], [90], [92], [93], [94], [95], [99], [111], [118]. Kinematic features, which were used in 20 publications, describe the motion of joints [21], [32], [45], [54], [58], [59], [60], [61], [62], [66], [69], [84], [85], [86], [87], [91], [92], [93], [94], [95]. For example, these can be the velocity, acceleration, or jerkiness of a joint.…”

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

“…Jerkiness is the derivative of acceleration and describes a motion's smoothness. Event-related features were used in 12 publications and characterize exercise-specific events [29], [31], [48], [50], [59], [60], [61], [62], [65], [84], [87], [91]. These can include the duration of an exercise or a sub-phase of an exercise (e.g., during the loading and release phases of a serve in tennis [31]).…”

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

“…Another popular category, with six publications using such algorithms, was filter methods. Primarily, they included statistical techniques, such as Pearson's Correlation and ANOVA, that were used to identify features with the highest correlation to the expected output [49], [69], [70], [84], [91]. Wrapper techniques were used in two publications by applying RL for feature learning [93], [94], and one publication that used forward feature selection [118].…”

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

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Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.

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Section: ) Preprocessingmentioning

confidence: 99%

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

Section: ) Feature Extraction Engineering and Selectionmentioning

confidence: 99%

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Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

et al. 2022

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Gamifying rehabilitation: MILORD platform as an upper limb motion rehabilitation service

Fotopoulos

Ladakis

Kilintzis

et al. 2022

Front. Comput. Sci.

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Motor learning is based on the correct repetition of specific movements for their permanent storage in the central nervous system (CNS). Rehabilitation relies heavily on the repetition of specific movements, and game scenarios are ideal environments to build routines of repetitive exercises that have entertaining characteristics. In this respect, the gamification of the rehabilitation program, through the introduction of game-specific techniques and design concepts, has gained attention as a complementary or alternative to routine rehabilitation programs. A gamified rehabilitation program promises to gain the patient's attention, to reduce the monotony of the process and preserve motivation to attend, and to create virtual incentives through the game, toward maintaining compliance to the “prescribed” program. This is often achieved through goal-oriented tasks and real-time feedback in the form of points and other in-game rewards. This paper describes MILORD rehabilitation platform, an affordable technological solution, which aims to support health professionals and enable remote rehabilitation, while maintaining health service characteristics and monitoring. MILORD is an end-to-end platform that consists of an interactive computer game, utilizing a leap motion sensor, a centralized user management system, an analysis platform that processes the data generated by the game, and an analysis dashboard presenting a set of meaningful features that describe upper limb movement. Our solution facilitates the monitoring of the patients' progress and provides an alternative way to analyze hand movement. The system was tested with normal subjects and patients and experts to record user's experience, receive feedback, identify any problems, and understand the system's value in monitoring and support motion defect and progress. This small-scale study indicated the capacity of the analysis to quantify the movement in a meaningful way and express the differences between normal and pathological movement, and the user experience was positive with both patients and normal subjects.

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Characterization of Upper Limb Functionality Caused by Neuromuscular Disorders using Novel Motion Features from a Specialized Gaming Platform

Cited by 2 publications

References 0 publications

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

Gamifying rehabilitation: MILORD platform as an upper limb motion rehabilitation service

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