Quantifying Dominant Upper-Limb Movement in Healthy and Painfull Shoulders

Coley, Brian; Jolles, B B; Farron, Alain; Pichonnaz, Claude; Bassin, JP; Aminian, Kamiar

doi:10.1016/s0021-9290(07)70104-5

Cited by 1 publication

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“…A quantifying method using inertial sensors and data loggers shows that subjects use dominant upper limb on average 18 percent more than the non-dominant upper limb for standing and sitting postures [12]. The high frequent use of the dominant limbs leads to the muscle strength imbalance between the dominant and non-dominant limbs.…”

Section: Introductionmentioning

confidence: 99%

Ankle injury assessment using inertial 3D data

Zhang

Naghdy

Stirling

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Sport injuries can prevent athletes from maintaining a high level of performance; keep them away from competitions during long periods of recovery, resulting in loss of income or even career. An objective system to assess the nature of the sport injury, adopt effective rehabilitation training at each stage and determine more effectively the time that the injured athletes can safely return to competition can reduce such liabilities. The diagnosis and monitoring of rehabilitation of sport injuries are critical for professional athletes. This study explores the feasibility of using inertial sensors to objectively aid in the assessment of sporting injuries and to monitor the progress made by an injured athlete during rehabilitation. This is proposed through observation, and contrasting the resultant deficits of muscle strength, and balance control, between healthy and injured parts of the body. The posture and gait of an athlete is monitored using a motion capture suit consisting of 17 tri-axial inertial sensor units. Three methods of Centre of Mass (COM), Symmetry angle (SA) and Gaussian Mixture Models (GMM) are applied to injured and control limbs, for a group from professional athletes when they perform single leg stance and one meter forward hopping. All of the three methods successfully distinguish between injured and control limbs. The results are presented and future extension of the methods is discussed.

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Section: Introductionmentioning

confidence: 99%