Adding Haptic Feedback to Virtual Environments With a Cable-Driven Robot Improves Upper Limb Spatio-Temporal Parameters During a Manual Handling Task

Feedback Type May Change the EMG Pattern and Kinematics During Robot Supported Upper Limb Reaching Task

Haptic interfaces and virtual reality (VR) technology have been increasingly introduced in rehabilitation, facilitating the provision of various feedback and task conditions. However, correspondence between the feedback/task conditions and movement strategy during reaching tasks remains a question. To investigate movement strategy, we assessed velocity parameters and peak latency of electromyography. Ten neuromuscularly intact volunteers participated in the measurement using haptic interface and VR. Concurrent visual feedback and various terminal feedback (e.g., visual, haptic, visual and haptic) were given. Additionally, the object size for the reaching task was changed. The results demonstrated terminal haptic feedback had a significant impact on kinematic parameters; showed [Formula: see text] s ([Formula: see text]) shorter movement time and [Formula: see text] m/s ([Formula: see text]) higher mean velocity compared to no terminal feedback. Also, smaller peak latency was observed in different muscle regions based on the object size.

Age-related differences in upper limb motor performance and intrinsic motivation during a virtual reality task

BACKGROUND

In recent years, virtual reality (VR) has evolved from an alternative to a necessity in older adults for health, medical care, and social interaction. Upper limb (UL) motor skill, is an important ability in manipulating VR systems and represents the brain's regulation of movements using the UL muscles. In this study, we used a haptic-feedback Virtual Box and Block Test (VBBT) system and an Intrinsic Motivation Inventory (IMI) to examine age-related differences in UL motor performance and intrinsic motivation in VR use. The findings will be helpful for the development of VR applications for older adults.

METHODS

In total, 48 young and 47 older volunteers participated in our study. The parameters including VBBT score, number of velocity peaks, velocity, grasping force and trajectory length were calculated to represent the task performance, manual dexterity, coordination, perceptive ability and cognitive ability in this study.

RESULTS

Age-related differences could be found in all the parameters (all p <  0.05) in VR use. Regression analysis revealed that the task performance of young adults was predicted by the velocity and trajectory length (R² = 64.0%), while that of older adults was predicted by the number of velocity peaks (R² = 65.6%). Additionally, the scores of understandability, relaxation and tiredness were significantly different between the two groups (all p <  0.05). In older adults, the understandability score showed large correlation with the IMI score (|r| = 0.576, p <  0.001). In young adults, the correlation was medium (|r| = 0.342, p = 0.017). No significant correlation was found between the IMI score and VBBT score (|r| = 0.142, p = 0.342) in older adults, while a medium correlation (|r| = 0.342, p = 0.017) was found in young adults.

CONCLUSIONS

The findings demonstrated that decreased smoothness in motor skills dominated the poor VR manipulation in older adults. The experience of understandability is important for older adults' intrinsic motivation in VR use.