Forward walking (FW) is a common balance assessment tool. However, its sensitivity is limited by the ceiling effect. Reverse gait, such as backward walking (BW), has been reported to have more advantages than FW for balance assessment. Three factors related to postural instability (i.e., increased speeds, restricted arm swing, and reduced visual feedback) during BW were investigated to determine BW conditions that have the potential to predict falls. Three-dimensional analyses were used to analyze seven walking conditions. FW and BW at self-selected and fast speeds were analyzed to identify the effects of speed. Walking with normal arm swings, crossed arms, and abducted arms during BW was tested to determine the effects of arm position. BW with closed and open eyes was compared to investigate the effects of visual feedback. BW had a significantly shorter step length than FW at high speeds. When the arms were abducted, the stance phase (%) was significantly lower compared to when arms were crossed during BW. Moreover, BW with closed eyes revealed significantly higher mediolateral center of mass (COM) displacements than with open eyes. We observed that BW with fast speeds, a crossed arm position, and closed eyes has the potential to help assess fall risk because it requires higher balance ability through spatiotemporal and COM adjustment.

Background: Although several signals have been proposed to control the activation timing of wearable robots, there is no consensus on the suitability of these signals in terms of their ability to promote effective cooperation and smooth device activation. Objective: This study investigated the effects of biomechanical (grip forces and voice commands), bioelectrical (electromyography [EMG]), and external sound (EX) signals on elbow flexion to trigger a wearable robot arm. Methods: Constant torque control was applied to a single‐degree‐of‐freedom (DOF) wearable robot arm, which operated within a range of 30°–130° of elbow flexion. Activation of the wearable robot arm was initiated using a grip force applied to a handle from either hand, the verbal command “go,” EMG signals, and an EX cue, while lifting a load equivalent to 15% of maximum voluntary contraction (MVC). Results: Prior to initiating the wearable robot arm, the grip force used to activate the movement using the hand on the side of the assisted arm significantly induced activities of the biceps brachii (BB) (%MVC _BB ) by approximately 6%–10%MVC _BB compared to bioelectrical and biomechanical signals. Nonetheless, after assistive forces were generated, the %MVC _BB elicited during the initial phase of elbow flexion was similar to other activation signals. In contrast, EMG‐induced activation increased the triceps brachii (TB) activity (%MVC _TB ) by approximately 7.2%MVC _TB during elbow flexion, resulting in the highest joint angular acceleration during the initial phase among all signals. Conclusion: These findings suggest that the grip force applied from the assistive hand was the most effective at triggering the wearable robot arm and may enhance muscle preparation and cooperation with the device more effectively.

Realistically simulating natural walking is essential to create an immersive virtual reality experience. However, differences between the virtual reality environment (VRE) and real environment (RE) can alter gait characteristics. Therefore, this study aimed to investigate differences in lower limb spatiotemporal parameters, joint kinematics, and muscle activity between overground walking in a VRE and RE. A total of 13 participants walked at 3 cadences (60 steps per minute [SPM], 80 SPM, and 100 SPM) in the VRE and RE. Motion capture and electromyography were employed to collect the spatiotemporal gait parameters, muscle activities of the right tibialis anterior and medial gastrocnemius muscles, and right lower limb joint angles of the participants. Our results showed that overground walking in the VRE altered several spatiotemporal gait parameters. In addition, the mean electromyography activity of the tibialis anterior and medial gastrocnemius muscles was reduced in the VRE during the initial double- and single-support phases. Most measured biomechanical parameters showed no significant interaction between the walking cadence and environment. This study clarifies biomechanical differences underlying the more cautious gait observed in a VRE, offering practical implications to enhance usability and optimize virtual reality locomotion design.

Obstacle crossing is a fundamental, yet complex, motor task commonly observed in daily life and occupational settings. Nevertheless, accurately replicating real-world crossing strategies in a virtual reality environment (VRE) remains challenging, potentially limiting the broader application of virtual reality (VR) in these domains. In this context, this study compared individuals’ gait and lower-limb kinematics during their performance of obstacle crossing in a real environment (RE) and a VRE to determine whether the latter alters their crossing behavior. Participants completed obstacle-crossing tasks over three obstacles (low: 100 mm, middle: 200 mm, and high: 300 mm) in both environments. Our results indicated that the participants’ overall crossing speed was reduced in the VRE (low obstacle: −9.0 cm/s; middle obstacle: −6.0 cm/s; high obstacle: −2.0 cm/s). Specifically, we observed that in the VRE, the leading leg exhibited greater lower-limb flexion (low obstacle: +13.8°; middle obstacle: +11.1°; high obstacle: +8.2°), which resulted in a higher toe clearance (low obstacle: +76.4 mm; middle obstacle: +40.0 mm; high obstacle: +23.5 mm). In addition, the horizontal distance from the peak vertical position was extended (low obstacle: +93.1 mm; middle obstacle: +79.0 mm; high obstacle: +73.8 mm). For the trailing leg, a trend similar to that of the crossing leg was observed under the VRE low-obstacle condition; however, with increasing obstacle height, the VRE showed smaller lower-limb flexion (high obstacle: −12.7°) and toe clearance than the RE (middle obstacle: −68.2 mm; high obstacle: −115.2 mm). These findings provide insight into gait adaptation in virtual contexts and support the design of more effective VR-based interventions for rehabilitation and fall prevention.