We present a learning-based intention detection methodology using a first-person-view camera for wearable hand robots.

Constraint-induced movement therapy (CIMT) is an effective recovery protocol for patients with stroke. However, not all the stroke patients can participate in CIMT. Devices that assist the movement of the affected side could help increase the number of stroke patients who can participate in CIMT like protocol. In this letter, a soft cable-driven wrist-wearable robot called ExoWrist, which assists the wrist of a paretic arm in performing the dart-throwing motion (DTM) with an active forearm anchor, is proposed. To increase the force transmission efficiency while preventing medical issues due to long-term pressure, a corset active anchor, which compresses the body only when anchoring is needed, is developed. A moving pulley mechanism and a three-layered structure increase the functionality of the anchor. Considering the natural movement of wrist extension and the frequently-used wrist motions in daily life, the wrist is assisted along the DTM direction, and the method to find a personal DTM is introduced. A 3D-printed custom wrist armlet is developed to hold the tendon path and exert a torque along the DTM direction. Together with a hand assistive device, Exo-Wrist could be used as a tool to help stroke patients with difficulty in wrist extension to participate in task-oriented rehabilitation protocols such as CIMT.

Soft wearable hand robots with tendon-sheath mechanisms are being actively developed to assist people with lost hand mobility. For these robots, accurately estimating fingertip forces leads to successful object grasping. An approach can utilize information from actuators assuming quasi-static environments. However, non-linearity and hysteresis with regards to the dynamic changes of the tendon-sheath mechanism hinder accurate fingertip force estimation. This paper proposes a learning-based method to estimate fingertip forces by integrating dynamic information of motor encoders, wire tension, and sheath bending angles. The model is modified from Long Short-Term Memory by incorporating a residual term that governs the dynamic changes in sheath bending angles. Using a tendon-driven soft wearable hand robot, the proposed model obtained RMSE less than 0.44 N. It was further evaluated under criteria ranging from different object sizes, bending angle ranges, and forces. Finally, a repeatability test (0.46 N in RMSE), real-time applicability (125 Hz), and force control (12.7% in MAPE) were performed to verify the feasibility of the proposed method.

The practical operation of wearable robots requires intuitive, compact, yet reliable control interfaces. However, current myoelectric interfaces based on surface electromyography (EMG) often fail to achieve these requirements by demanding multiple sensors and exhibiting unreliable performance under limb posture changes. In this study, we show that a myoelectric interface on the musculotendinous junctions (MTJs) of the flexor digitorum superficialis (FDS) enables reliable control of a robotic glove with a single EMG sensor by identifying power grasp intentions. We found that the myoelectric signals from the MTJs of the FDS show significantly increased amplitudes exclusively when a power grasp is performed, regardless of the arm posture. We systematically verified that, in identifying power grasp intentions, the proposed single-sensor myoelectric interface even outperforms a five-sensor myoelectric interface around the proximal forearm. By exploiting the unique biological feature of the MTJs, we devised two myoelectric control methods for a robotic glove—Dual-threshold control and Morse-code control—and further showed their performances in practical operations. Dual-threshold control enables direct co-operation between the user and the robotic glove, and Morse-code control provides various command options for the user.

Input-shaping control has received considerable research attention for suppressing residual vibrations. Although numerous studies have been conducted on designing input shapers with arbitrary robustness to modeling errors, no studies have focused on the design of input shapers with arbitrarily specified shaping times. In this study, a specified-duration (SD) shaper, which is an input shaper with an arbitrarily specified shaping time, and a systematic method to design an SD shaper using impulse vectors are proposed. As the specified shaping time increases, the SD shaper increases the number of impulses one by one according to the number of added derivative constraints, thereby improving robustness to modeling errors. The performance of the SD shaper was evaluated for a second-order system through computer simulations. The simulation results revealed that the SD shaper suppresses residual vibrations of the vibratory system at the specified shaping time. The validity of the SD shaper was experimentally verified using a horizontal beam vibration apparatus. The results of this study provide insight into the development of vibration suppression strategies with input shaping control.

To assist the walk of the physically weak people is important to increase their quality of life because it can expand their activity area. We present a soft wearable knee extensor, called Knee Tendon-suit, which assist the knee extension during climbing stairs. To achieve our goal, we used the tendon-driven mechanism for transmitting the actuation force and the anchor structure for transmitting the actuation force to the body. Measuring Electromyography (EMG) at the femoral muscles and the tension at the anchor, we verified performance of the Knee Tendon-suit as the first preliminary result.