Visual feedback training of a movement assistance human-machine interface

Our objective is to train the user on machine-predictable movements
​for a dynamic rehabilitation task aimed at more effective device control

Visual Feedback for Rehabilitation and Movement Assistance Training

Visual feedback (VF) can be presented in a myriad of modes, depending on the timing, style of display, and performance metric objectively targeted. VF outperforms audio and haptic feedback for training user spatial positioning of complex motor-tasks [1]. Our interests are in developing VF paradigms that effectively train the user to complete a dynamic rehabilitation task for more effective assist device control.

Optitrack Motion Capture Analysis
Retroreflective marker position are captured by infrared cameras for tracking user spatial positioning

Concurrent Visual Feedback
Real-time feedback of a performance metric while the user completes the task

Terminal Visual Feedback
Feedback presented immediately following task-completion

Visual Feedback Training for a Two-legged Squat Exercise

VF was presented in a variety of forms for training the subjects on a dynamic rehabilitation task. The objective was to determine the visual feedback mode with the greatest reduction in movement error over a single training session. The winning VF trained the greatest consistency of spatial positioning and muscle activation patterns.

Concurrent Visual Feedback
An example of a VF presented for the squat exercise. Categorized as 'Continuous Complex', the user's spatial positioning (black) of the shank, thigh, and torso segments, are simultaneously displayed with the target trajectory (red). As the subject squats his objective is to reduce the error between their spatial position and the target position.

Rectus Femoris Muscle Activation Influenced by Changes in Squat Technique

The two legged squat exercise is clinically correlated to the sit-to-stand movement. Muscle activation patterns are highly influenced by technique and are captured in real-time by surface electromyography sensors on lower-limb muscles primarily responsible for force generation. Training the user to do a complex rehabilitation task in a more consistent movement pattern will induce consistent muscle activation patterns.

Identification of User-Specific Optimal Parameters by Musculoskeletal Modeling

(Coming Soon)


1. R. Sigrist, G. Rauter, R. Riener, and P. Wolf, “Augmented visual, auditory, haptic, and multimodal feedback in motor learning: a review,” Psychon. Bull. Rev., vol. 20, no. 1, pp. 21–53, Feb. 2013.​