Date of Award
Health, Exercise Science, and Recreation Management
Velocity-based training (VBT) is a method of resistance training (RT) that monitors movement velocity during exercise in order to prescribe future RT more accurately than common methods such as percentage-based training (PBT) (Weakley, 2020). VBT and other forms of RT have been developed to address several of PBT’s shortcomings which include failure to account for an individual’s day-to-day strength, a lack of variation between individual participants, and even delayed adjustment to strength gains as a result of training (Weakley, 2020). VBT, specifically, allows for a more precise and objective quantification of RT intensity because velocity is a logical indicator of neuromuscular fatigue (Nevin, 2019), and the method can also be used to provide feedback that can enhance performance, motivation, and competitiveness (Weakley, 2020). While the implementation of VBT has shown increased benefits compared to PBT (Dorrell, 2020; Nevin, 2019), the method requires the use of a velocity measurement device such as a linear position transducer (LPT) or an optical measurement device. A variety of these measurement devices have been extensively studied to ensure they provide accurate measurement data, and, at the moment, LPTs including the GymAware Power Tool (GYM), 1080 Quantum, and Vmaxpro are some of the most reliable, tested devices (Fritschi, 2021; Orange, 2020). Few optical measurement devices are supported by research, but one new optical device is the Perch velocity measurement system. In order to assess its validity, this study recruited resistance trained females between the ages of 18 and 35 to perform sets of deadlifts while being observed by Perch and a 3D motion capture system. The velocities obtained by each system were compared to one other, and the Perch system was determined to be reliable with calculated Pearson correlation coefficients of 0.806 (mean velocity) and 0.908 (peak velocity).
Rowell, Becht, "Assessing the Validity of the Perch System During a Deadlift" (2022). Honors Theses. 2614.
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