Let the force guide you: a performance-based adaptive algorithm for postural training using haptic feedback - Agarwal, Rakhi; Hussain, Asif; SKM, Varadhan; Campolo, Domenico (Frontiers in Human Neuroscience, 2022)
Motor learning is an essential component of human behavior. Many different factors can influence the process of motor learning, such as the amount of practice and type of feedback. Changes in task difficulty during the training can also considerably impact motor learning. Typical motor learning studies include a sequential variation of task difficulty, i.e., easy to challenging, irrespective of user performance. However, many studies have reported the importance of performance-based task difficulty variation for effective motor learning and skill transfer. A performance-based adaptive algorithm for task difficulty variation based on the challenge-point framework is proposed in this study. The algorithm is described for postural adaptation during simultaneous upper-limb training. Ten healthy participants (28 ± 2.44 years) were recruited to validate the algorithm. Participants adapted to a postural target of 20o in the anterior direction from the initial upright posture while performing a unimanual reaching task using a robotic device. Results suggest a significant decrease in postural error after training. The algorithm successfully adapted the task difficulty based on the performance of the user. The proposed algorithm could be modified for different motor skills and can be further evaluated for different applications in order to maximize the potential benefits of rehabilitation sessions