Augmenting human power by assistive robots: Application of adaptive neural networks

Asl, Hamed Jabbari; Katagiri, Kota; Narikiyo, Tatsuo; Kawanishi, Michihiro

doi:10.1016/j.conengprac.2021.104769

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Kao et al tried to evaluate the activation of muscle, Jackson and Collins utilized muscular activation, and Lee et al tried to evaluate the reduction of metabolic cost [17][18][19]. Chris McGibbon et al tried to evaluate physical performance with/without the wearable robot, Stefano Toxiri et al evaluated the effectiveness of the wearable robot as regards how much the structures and physical attachments transfer force or toque to the wearer, Antonio Di Lallo et al tried to evaluate mechanical impedance, high back drivability, and high torque, Mona Bar et al evaluated physical stress and strain, and Homed Jabbari Asl et al tried to evaluate the tracking accuracy and the smoothness of the system [20][21][22][23][24]. However, these approaches evaluate only one or two wearers at the time of development by comparing parameters including metabolic cost, muscle activity, physical performance, structures, etc.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of the Effectiveness of Wearable Robot

Lee

Cha

2021

Electronics

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In this paper, we present a new statistical approach for evaluating the time-dependent effectiveness of wearable robots without real work. In total, 10 subjects participated in three phases of the experiment; not equipped with a wearable robot without any load, not equipped with the wearable robot with a 15 kg load, equipped with the wearable robot with a 15 kg load. A higher limb wearable robot called LEXO-W was utilized. We measured the time taken to complete a 10 m round trip 10 times as a lap time, and each participant was measured multiple times under all conditions. An increasing number of round trips causes an increment in lap times. In particular, the load-carrying group showed a rapid upward trend in lap time over the number of round trips. However, the robot-assisted group showed a slightly upward trend of lap time over the number of round trips. This study statistically shows that the LEXO-W helps reduce physical fatigue by using repeated measure ANOVA analysis. Furthermore, we employed the generalized additive model(GAM) model to predict and evaluate the effectiveness of the wearable robot.

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