Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperie...
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Age-associated changes in walking parameters are relevant to recognize functional capacity and physical performance. However, the sensible nuances of slightly different gait patterns are hardly noticeable by inexperienced observers. Due to the complexity of this evaluation, we aimed at verifying the efficiency of applied hybrid-adaptive algorithms to cluster groups with similar gait patterns. Based on self-organizingmaps (SOM), k-means clustering (KM), and fuzzy c-means (FCM), we compared the hybrid algorithms to a conventional FCM approach to cluster accordingly age-related groups. Additionally, we performed a relevance analysis to identify the principal gait characteristics. Our experiments, based on inertial-sensors data, comprised a sample of 180 healthy subjects, divided into age-related groups. The outcomes suggest that our methods outperformed the FCM algorithm, demonstrating a high accuracy (88%) and consistent sensitivity also to distinguish groups that presented a significant difference (p < .05) only in one of the six observed gait features. The applied algorithms showed a compatible performance, but the SOM + KM required less computation cost and, therefore, was more efficient. Furthermore, the results indicate the overall importance of cadence, as a measurement of physical performance, especially when clustering subjects by their age. Such output provides valuable information to healthcare professionals, concerning the subject's physical performance related to his age, supporting and guiding the physical evaluation. (C) 2020 IPEM. Published by Elsevier Ltd. All rights reserved.
Human gait corresponds to the physiological way of locomotion, which can be affected by several injuries. Thus, gait analysis plays an important role in observing kinematic and kinetic parameters of the joints involve...
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ISBN:
(纸本)9783319534800;9783319534794
Human gait corresponds to the physiological way of locomotion, which can be affected by several injuries. Thus, gait analysis plays an important role in observing kinematic and kinetic parameters of the joints involved with such movement pattern. Due to the complexity of such analysis, this paper explores the performance of two adaptive methods, Fuzzy c-means (FCM) and self-organizingmaps (SOM), to simplify the interpretation of gait data, provided by a secondary dataset of 90 subjects, subdivided into six groups. Based on inertial measurement units (IMU) data, two kinematic features, average cycle time and cadence, were used as inputs to the adaptive algorithms. Considering the similarities among the subjects of such database, our experiments show that FCM presented a better performance than SOM. Despite the misplacement of subjects into unexpected clusters, this outcome implies that FCM is rather sensitive to slight differences in gait analysis. Nonetheless, further trials with the aforementioned methods are necessary, since more gait parameters and a greater sample could reveal an undercover variation within the proper walking pattern.
This paper describes a dynamic distributed monitoring scheduling algorithm (SOMSA) for sensor networks based on artificial neural-networks self-organizing maps algorithm. During the training, the stable sensor nodes c...
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ISBN:
(纸本)9781424421077
This paper describes a dynamic distributed monitoring scheduling algorithm (SOMSA) for sensor networks based on artificial neural-networks self-organizing maps algorithm. During the training, the stable sensor nodes compete and cooperate with their neighbors to study the input patterns of the environment and update their internal prototype vectors using only local sensing and interactions. The adjustment stops gradually if the environment is changeless or gradually changed. In the end, the networks monitoring patterns will reflect the feature of the environment. Simulation indicates that this is a valid methodology, being especially promising for energy saving with limited resources.
This paper describes a dynamic distributed monitoring scheduling algorithm (SOMSA) for sensor networks based on artificial neural-networks self-organizing maps algorithm. During the training, the stable sensor nodes c...
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This paper describes a dynamic distributed monitoring scheduling algorithm (SOMSA) for sensor networks based on artificial neural-networks self-organizing maps algorithm. During the training, the stable sensor nodes compete and cooperate with their neighbors to study the input patterns of the environment and update their internal prototype vectors using only local sensing and interactions. The adjustment stops gradually if the environment is changeless or gradually changed. In the end, the networks monitoring patterns will reflect the feature of the environment. Simulation indicates that this is a valid methodology, being especially promising for energy saving with limited resources.
Sensor deployment is an important problem in mobile wireless sensor *** paper presents a dis-tributed self-spreading deployment algorithm(SOMDA)for mobile sensors based on artificial neural-networks self-organizing ma...
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Sensor deployment is an important problem in mobile wireless sensor *** paper presents a dis-tributed self-spreading deployment algorithm(SOMDA)for mobile sensors based on artificial neural-networks self-organizingmaps *** the deployment,the nodes compete to track the event and cooperate to form an ordered *** going through the algorithm,the statistical distribution of the nodes approaches that of the events in the interest *** performance of the algo-rithm is evaluated by the covered percentage of re-gion/events,the detecting ability and the energy equaliza-tion of the *** simulation results indicate that SOMDA outperforms uniform and random deployment with lossless coverage,enhancive detecting ability and signifi-cant energy equalization.
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