A survey of wearable sensor networks in health and entertainment

Olson, Jason S

doi:10.15406/mojabb.2018.02.00082

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…is kind of somatosensory game completely breaks the bondage of the handle button, using the human motion capture device to capture the gamer's movement and directly use it as the game's input to control the game character's movement [1].…”

Section: Introductionmentioning

confidence: 99%

Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

2021

Complexity

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In this paper, through an in-depth study and analysis of dance motion capture algorithms in wearable sensor networks, the extended Kalman filter algorithm and the quaternion method are selected after analysing a variety of commonly used data fusion algorithms and pose solving algorithms. In this paper, a sensor-body coordinate system calibration algorithm based on hand-eye calibration is proposed, which only requires three calibration poses to complete the calibration of the whole-body sensor-body coordinate system. In this paper, joint parameter estimation algorithm based on human joint constraints and limb length estimation algorithm based on closed joint chains are proposed, respectively. The algorithm is an iterative optimization algorithm that divides each iteration into an expectation step and a great likelihood step, and the best convergence value can be found efficiently according to each iteration step. The feature values of each pose action are fed into the algorithm for model learning, which enables the training of the model. The trained model is then tested by combining the collected gesture data with the algorithmic model to recognize and classify the gesture data, observe its recognition accuracy, and continuously optimize the model to achieve accurate recognition of human gesture actions.

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Section: Introductionmentioning

confidence: 99%

Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

2021

Complexity

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“…7,8 IMU sensor has positive impact on the patients and the healthy users of the activity recognition systems the depend on it to function; such as the systems that prevent elderly from falling, human gait analysis, and long-term heath monitoring system. 9 Using IMU sensor in these systems are safe, as no recent study has shown any biological or psychological impact on the users on the long term, according to the authors' research.…”

Section: Introductionmentioning

confidence: 94%

Sensor positioning for a human activity recognition system using a double layer classifier

Abdelhafiz

Awad

Sadek

et al. 2021

Proc Inst Mech Eng H

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This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor’s optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.

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“…For example, the sensor data could be used to generate finer details about movement abnormality and identification of compensatory movements (alternative movement strategies to circumvent impairment). In these cases, the sensor motion data are collected and analyzed from Wearable Sensors Networks (WSNs) [ 7 ], which are defined by a set of interconnected sensors dedicated to collecting physiological parameters in the patient’s body. This technology can be used, for example, to diagnose patients through telemedicine applications [ 6 ] to analytically detect the compensatory actions of patients in rehabilitation [ 8 ], and others [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

Contreras

Parnandi

Coelho

et al. 2021

Sensors

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A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.

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A survey of wearable sensor networks in health and entertainment

Cited by 17 publications

References 23 publications

Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

Dance Motion Capture Based on Data Fusion Algorithm and Wearable Sensor Network

Sensor positioning for a human activity recognition system using a double layer classifier

NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

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