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Sports franchises invest a lot in training their athletes. use of latest technology for this purpose is also very common. We propose a system of capturing motion of athletes during weight training and analyzing that data to find out any…
Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and…
Maximum entropy has become a mainstream off-policy reinforcement learning (RL) framework for balancing exploitation and exploration. However, two bottlenecks still limit further performance improvement: (1) non-stationary Q-value estimation…
Human walkers traverse diverse environments and demonstrate different gait locomotion and energy cost on granular terrains compared to solid ground. We present a stiffness-based model predictive control approach of knee exoskeleton…
Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem. This method aims to recover global motions from a set of relative motions, where the original method is sensitive…
In recent decades, running has become an increasingly popular pastime activity due to its accessibility, ease of practice, and anticipated health benefits. However, the risk of running-related injuries is substantial for runners of…
The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are…
In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers. To refrain from ad-hoc schemes and to find maximally discriminative features we may need…
In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indicate the occurrence of healthcare-related events. Three…
This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running)…
Within the last decade, running has become one of the most popular physical activities in the world. Although the benefits of running are numerous, there is a risk of Running Related Injuries (RRI) of the lower extremities. Electromyography…
Biomechanics and human movement research often involves measuring multiple kinematic or kinetic variables regularly throughout a movement, yielding data that present as smooth, multivariate, time-varying curves and are naturally amenable to…
This paper addresses several critical stages of designing a walking robot, including optimal structural synthesis, introducing a novel 'rational' mechanical structure aimed at enhancing efficiency and simplifying control system, while…
Neurological disorders, including stroke, spinal cord injuries, multiple sclerosis, and Parkinson's disease, generally lead to diminished upper extremity (UE) function, impacting individuals' independence and quality of life. Traditional…
The study of biomechanics during locomotion provides valuable insights into the effects of varying conditions on specific movement patterns. This research focuses on examining the influence of different shoe parameters on walking…
During the past century, biologists and mathematicians investigated two mechanisms underlying bacteria motion: the run phase during which bacteria move in straight lines and the tumble phase in which they change their orientation. When…
Lactate threshold is considered an essential parameter when assessing performance of elite and recreational runners and prescribing training intensities in endurance sports. However, the measurement of blood lactate concentration requires…
Energy efficiency is a critical factor in the performance and autonomy of quadrupedal robots. While previous research has focused on mechanical design and actuation improvements, the impact of gait parameters on energetics has been less…
This study proposes an exercise fatigue detection model based on real-time clinical data which includes time domain analysis, frequency domain analysis, detrended fluctuation analysis, approximate entropy, and sample entropy. Furthermore,…
Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning…