Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review

Du, Jiaying; Gerdtman, Christer; Lindén, María

doi:10.3390/s18041123

Cited by 26 publications

(19 citation statements)

References 31 publications

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“…On the other hand, for the wearable sensors (accelerometer and gyroscope) noise might be due to temperature change, shock, or vibration. The noise needed to be reduced well as it affects the classification [15]. Moreover, according to [16] Kalman filter did achieve a good rate of denoise different sensors (IR depth camera, accelerometer, and gyroscope).…”

Section: Filtration Processmentioning

confidence: 99%

Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes

Hegazy¹

2021

IJIES

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Sports training generally focuses on speed of response and variety of strategies aimed at encouraging sustainable physical activity and improving learning skills for players, thus, enhance their performance and skills through matches. In this paper, we are presenting a methodology for multi-sensor fusion method of IR depth camera and smart band sensors using curve fitting to recognize and group the characteristics of strokes performed by players and adapt the training accordingly. The main aim of the methodology is to classify various techniques played in table tennis and enhance the strokes based on various body joints. Moreover, the main contribution of this paper is to experiment different sensors to get the most optimal tools in classifying players strokes. Also, to test various classification algorithms to get the optimal and best result possible. Overall, based on the experiments we have concluded that sensor fusion between internal sensors and IR depth camera has increased the classification results and robustness of the solution. The system's results indicate an average accuracy of 95%-100%.

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Section: Filtration Processmentioning

confidence: 99%

Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes

Hegazy¹

2021

IJIES

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“…We must be aware of important problems involving the accuracy of the computed motion signals. These problems are the result of accelerometer errors, but are primarily associated with the phenomenon of the gyroscope drift [41,42]. Due to space limitations, we do not discuss these issues.…”

Section: The Motion Sensors and Preprocessing Of Their Signalsmentioning

confidence: 99%

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions

Wójcik

Piekarczyk

2020

Sensors

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The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms.

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“…The rotation from the gyroscope is provided as a quaternion q s = (v x , v y , v z , k). It allows for the calculation of a rotation matrix, let it be denoted by A, representing the sensor's orientation expressed in an Earth-fixed reference frame [31]:…”

Section: A the Motion Sensorsmentioning

confidence: 99%

Pattern Recognition Methods as a Tool to Build an Automatic System for Learning Coordinated Human Motions

Wójcik

2020

IEEE Access

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There is great social and economic significance in the teaching and learning of motion activities. This is notably true for teaching the activities involved in rehabilitation, sports, and professional work. The possibility of engaging an automatic teaching system is highly significant. Nevertheless, building an effective system is an ongoing challenge. This article describes a general outline of the teaching system, which includes MEMS (micro-electro-mechanical systems) sensors, haptic actuators, and algorithms for signal classification applied to the online selection of an appropriate teaching method. The main goal of this paper was to prove that the system is able to teach fast and synchronized movements effectively. To this end, system performance was presented and discussed. The statistical tests revealed an efficiency of the proposed approach, especially for tasks of teaching fast and periodic movements. This result was the primary outcome of the presented paper. The described scheme can be utilized for building two types of motor learning systems. The first relates to the "personal" learning systems for rehabilitation and sports. The second type can perform the classification of complex movements of human body parts and may be used in teaching the remote control of machines and vehicles (excavators, cranes, search and rescue drones, etc.).INDEX TERMS haptic feedback, machine learning, MEMS sensors, motor learning, pattern recognition.

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Signal Quality Improvement Algorithms for MEMS Gyroscope-Based Human Motion Analysis Systems: A Systematic Review

Cited by 26 publications

References 31 publications

Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes

Multi-Sensor Fusion for Online Detection and Classification of Table Tennis Strokes

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions

Pattern Recognition Methods as a Tool to Build an Automatic System for Learning Coordinated Human Motions

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