Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints
Thông tin tác giả: TS. Preecha Yupapin, Trưởng nhóm Nhóm nghiên cứu quang học tính toán thuộc Trường đại học Tôn Đức Thắng (CORG).
Tên bài báo: Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological constraints
Thông tin tạp chí: Bài báo được công bố trên tạp chí ISI BMC Research Notes (impact factor là 0.608 theo ISI và chỉ số H-index là 47 theo SJR).
An effective training plan is an important factor in sports training to enhance athletic performance. A poorly considered training plan may result in injury to the athlete, and overtraining. Good training plans normally require expert input, which may have a cost too great for many athletes, particularly amateur athletes. The objectives of this research were to create a practical cycling training plan that substantially improves athletic performance while satisfying essential physiological constraints. Adaptive Particle Swarm Optimization using ɛ-constraint methods were used to formulate such a plan and simulate the likely performance outcomes. The physiological constraints considered in this study were monotony, chronic training load ramp rate and daily training impulse.
A comparison of results from our simulations against a training plan from British Cycling, which we used as our standard, showed that our training plan outperformed the benchmark in terms of both athletic performance and satisfying all physiological constraints.
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