In practical rehabilitation robot development, it is imperative to pre-specify the critical workspace to prevent redundant structure. This study aimed to characterize the upper extremity motion during essential activities in daily living. An IMU-based wearable motion capture system was used to access arm movements. Ten healthy subjects performed the Action Research Arm Test (ARAT) and six pre-selected essential daily activities. The Euler angles of the major joints, and acceleration from wrist and hand sensors were acquired and analyzed. The size of the workspace for the ARAT was 0.53 (left-right) × 0.92 (front-back) × 0.89 (up-down) m for the dominant hand. For the daily activities, the workspace size was 0.71 × 0.70 × 0.86 m for the dominant hand, significantly larger than the non-dominant hand (p ≤ 0.011). The average range of motion (RoM) during ARAT was 109.15 ± 18.82° for elbow flexion/extension, 105.23 ± 5.38° for forearm supination/pronation, 91.99 ± 0.98° for shoulder internal/external rotation, and 82.90 ± 22.52° for wrist dorsiflexion/volarflexion, whereas the corresponding range for daily activities were 120.61 ± 23.64°, 128.09 ± 22.04°, 111.56 ± 31.88°, and 113.70 ± 18.26°. The shoulder joint was more abducted and extended during pinching compared to grasping posture (p < 0.001). Reaching from a grasping posture required approximately 70° elbow extension and 36° forearm supination from the initial position. The study results provide an important database for the workspace and RoM for essential arm movements.

Robotic devices that practically assist activities of daily living (ADL) are scarce. The aim of this study was to investigate practical demands of potential users of external robotic arms and upper limb exoskeletons for assistance in ADL. A survey was performed in rehabilitation clinics in individuals with functional impairments in the upper extremity, divided into unilateral (UIG, n = 24) and bilateral impairment groups (BIG, n = 24). Descriptive analyses were performed for current dependency, objective importance, and subjective necessity of the 18 ADLs by using a 5-point Likert scale. Overall, handling foods, dressing, and moving close items were highly necessary functions for both robot types. The UIG demonstrated a high demand for self-exercise using exoskeletons, whereas one-hand ADLs showed low necessity. In the UIG, the exoskeleton had significantly higher demands than the external robotic arm in washing face (p = 0.005) and brushing teeth (p = 0.007). The subjects in the BIG replied that cleaning desks and eating are highly necessary abilities for the external robotic arm; and transfer and wheelchair control, for exoskeletons. In the BIG, the exoskeleton showed significantly higher necessity than the external robotic arms in dressing (p = 0.010), making phone calls (p = 0.026), using a smartphone (p = 0.011), and writing (p = 0.005). The practical demands of potential users were affected by laterality and robot type. Further robot developments should involve essential functions based on the survey results to meet end-user needs.

While spontaneous robotic arm control using motor imagery has been reported, most previous successful cases have used invasive approaches with advantages in spatial resolution. However, still many researchers continue to investigate methods for robotic arm control with noninvasive neural signal. Most of noninvasive control of robotic arm utilizes P300, steady state visually evoked potential, N2pc, and mental tasks differentiation. Even though these approaches demonstrated successful accuracy, they are limited in time efficiency and user intuition, and mostly require visual stimulation. Ultimately, velocity vector construction using electroencephalography activated by motion-related motor imagery can be considered as a substitution. In this study, a vision-aided brain–machine interface training system for robotic arm control is proposed and developed. The proposed system uses a Microsoft Kinect to detect and estimates the 3D positions of the possible target objects. The predicted velocity vector for robot arm input is compensated using the artificial potential to follow an intended one among the possible targets. Two participants with cervical spinal cord injury trained with the system to explore its possible effects. In a situation with four possible targets, the proposed system significantly improved the distance error to the intended target compared to the unintended ones (p < 0.0001). Functional magnetic resonance imaging after five sessions of observation-based training with the developed system showed brain activation patterns with tendency of focusing to ipsilateral primary motor and sensory cortex, posterior parietal cortex, and contralateral cerebellum. However, shared control with blending parameter α less than 1 was not successful and success rate for touching an instructed target was less than the chance level (= 50%). The pilot clinical study utilizing the training system suggested potential beneficial effects in characterizing the brain activation patterns.

Nam HS, et al. J Korean Med Sci. 2017 Oct;32(10):1568-1575. https://doi.org/10.3346/jkms.2017.32.10.1568