Stroke is one of the leading causes of upper extremity disability around the world. Whenever a stroke happens stroke survivor’s brain commands cannot reach some muscles of upper extremities although those muscles could contract. Therefore, shoulder, elbow or wrist joint cannot perform expected motion, and this will hamper their activities of daily living (ADLs). The objective of rehabilitation is to externally drive the upper extremity move to improve muscle movements. The current state of upper extremity rehabilitation may improve by using a model-based computer simulation of arm movement for personalizing robotic devices and interventions. This study attempts to review technologies used in upper extremity rehabilitation on two aspects: computer models and robotic devices. A summary of existing virtual upper extremity models is provided. As well, different robotic devices that are developed for upper limb rehabilitation is also discussed here.

Box delivery is a complicated task and it is challenging to predict the box delivery motion associated with the box weight, delivering speed, and location. This paper presents a single task-based inverse dynamics optimization method for determining the planar symmetric optimal box delivery motion (multi-task jobs). The design variables are cubic B-spline control points of joint angle profiles. The objective function is dynamic effort, i.e., the time integral of the square of all normalized joint torques. The optimization problem includes various constraints. Joint angle profiles are validated through experimental results using root-mean-square-error (RMSE) and Pearson’s correlation coefficient. This research provides a practical guidance to prevent injury risks in joint torque space for workers who lift and deliver heavy objects in their daily jobs.

Cerebrovascular accidents like a stroke can affect lower limb as well as upper extremity joints (i.e., shoulder, elbow or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion-extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results.

Box delivery is a complicated manual material handling task which needs to consider the box weight, delivering speed, stability, and location. This paper presents a subtask-based inverse dynamic optimization formulation for determining the two-dimensional (2D) symmetric optimal box delivery motion. For the subtask-based formulation, the delivery task is divided into five subtasks: lifting, the first transition step, carrying, the second transition step, and unloading. To render a complete delivering task, each subtask is formulated as a separate optimization problem with appropriate boundary conditions. For carrying and lifting subtasks, the cost function is the sum of joint torque squared. In contrast, for transition subtasks, the cost function is the combination of joint discomfort and joint torque squared. Joint angle profiles are validated through experimental results using Pearson’s correlation coefficient (r) and root-mean-square-error (RMSE). Results show that the subtask-based approach is computationally efficient for complex box delivery motion simulation. This research outcome provides a practical guidance to prevent injury risks in joint torque space for workers who deliver heavy objects in their daily jobs.

Cerebrovascular accidents like a stroke can affect lower limb as well as upper extremity joints (i.e., shoulder, elbow or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate force reduces, thus affecting the joint’s torque production. Understanding how muscles generate force is a key element to injury detection. Researchers developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this research is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces were used to determine the elbow joint torque. Experimental EMG signals and motion capture data were collected for a healthy subject. The musculoskeletal model was scaled to match the geometric parameters of the subject. First, the approach calculated muscle forces and joint moment for simple elbow flexion-extension. Later, the same approach was applied to an exercise called triceps kickback, which trains the triceps muscle group. Individual muscle forces and net joint torques for both tasks were estimated.