Yonatan Hutabarat 교수

The current gold standard for gait analysis involves performing the gait experiments in a laboratory environment with a constrained space. However, there is growing interest in using flexible, efficient, and inexpensive wearable sensors as tools to perform gait analysis. This review aimed to identify and summarize the current advances in wearable sensors for various aspects of gait analysis, such as the application of wearable gait analysis systems, sensor systems and their attachment locations,

The importance of gait analysis in medical applications, such as in rehabilitation, has been widely studied. Wearable sensors have gained popularity owing to their convenience of use in a flexible environment, while providing accuracy and reliability, in comparison with the gold standard system, i.e., motion capture. In this study, we proposed a framework for quantitative gait assessment using only two inertial measurement unit (IMU) sensors, while extracting maximum number of features. Decreasi

In this study, we investigated a control algorithm for a semi-active prosthetic knee based on reinforcement learning (RL). Model-free reinforcement Q-learning control with a reward shaping function was proposed as the voltage controller of a magnetorheological damper based on the prosthetic knee. The reward function was designed as a function of the performance index that accounts for the trajectory of the subject-specific knee angle. We compared our proposed reward function to a conventional si

In this study, we proposed a framework for extracting gait events and extensive temporal features, seamlessly, during walking and running on a treadmill by constructing a finite state machine (FSM) transition rules based on two IMU sensors attached to the back of the shoes. Detailed innerclass states were defined to recognize the double support phase on walking gait and the double flight phase on running gait. Further, an in-depth speed-based analysis of temporal gait features can be performed f

The presence of static friction or stiction in a valve can lead to poor performance in industrial automation and control system. This paper presents a method to detect and quantify stiction based on normality test and Hammerstein system identification. Detection and quantification are showed by the fs and fd parameters, where the value of fs and fd greater than 0 indicate the valve suffer from stiction. The proposed method is implemented to a confirmed sets of industrial data as a validation. Th

Multiple tasks are simultaneously performed during walking in our daily life. Distracted walk by smartphone usage is recently getting a social problem. The term dual-task gait refers to the secondary task added to the walking. Attention demanding tasks may influence how a person walks. Since in-lab measurement may not accurately reflect the daily living gait, wearable sensors approach have been proposed for gait analysis in an out-of-lab setting. This study addresses the potential of using only

This paper presents a knee torque estimation in non-pathological gait cycle at stance phase. Comparative modelling by using dynamics model and neural network model is discussed. Dynamics modelling is constructed by using simple two degree of freedom dynamics with Newtonian calculation approach and more complex four degree of freedom dynamics with Lagrangian calculation approach. Neural network based model is constructed with feed-forward neural network (FNN) structure using six different kinemat

This paper presents an approach to detect phase transition from sit to stand (STS) movement in able bodied person based on knee angle and ground reaction force (GRF) data. Phase transition detection method developed in this paper is a heuristic rule based algorithm for the purpose of prosthetic knee device that have the limitation of number of parameters that can be obtained in the amputee. A single able-bodied subject experimental design is performed to obtained necessary data to construct the

Abstract Evading danger is critical to survival. In non-human animals, escape strategies are shaped by neural, biomechanical, and ecological constraints, resulting in species-specific patterns. In humans, ethical and practical constraints have until recently hindered investigation of escape movements, such that its organising principles are commonly extrapolated from other, mostly quadruped, species. Here, we use wireless virtual reality (W-VR) in a large physical space to present biologically r