Wearable Hand-Rehabilitation System with Soft Gloves for Patient with Face Paralysis and Disability

Artificially intelligent advances such as tech gloves allow handicapped wearers to handle daily matters as normal. A wearable hand-rehabilitation system, i.e., a robotic arm, is engineered with controlled programming to control a disabled hand with features such as movement of fingers and holding items. A life-threatening disease (stroke) is caused when brain cells start to die, causing around 50–70% of patients to face paralysis and disability. People may face after-effects such as reduced use of the hand and limb or a paralyzed hand. Many methods have been introduced to overcome these issues, including therapies, but they are not so reliable when overcoming disability issues. To overcome these issues, we proposed a smart robotic hand that encounters hand disability issues. The smart robotic hand will aid the hands of disabled people by replacing their disabled hand with the smart robotic hand and by controlling the movement of the robot with the movement of the other hand. This can also be helpful for environments where it is not feasible for humans to work, such as in nuclear reactors and in bomb disposal squads. Some people have disabilities of the hand, so this smart robotic hand can also be used in that scenario. The robotic hand is mainly controlled through a flex sensor. By using Arduino, flex sensor outputs are mapped accordingly to the servo motors. The robot is controlled by a wired arrangement.

A Novel Clinical-Driven Design for Robotic Hand Rehabilitation: Combining Sensory Training, Effortless Setup, and Large Range of Motion in a Palmar Device

Neurorehabilitation research suggests that not only high training intensity, but also somatosensory information plays a fundamental role in the recovery of stroke patients. Yet, there is currently a lack of easy-to-use robotic solutions for sensorimotor hand rehabilitation. We addressed this shortcoming by developing a novel clinical-driven robotic hand rehabilitation device, which is capable of fine haptic rendering, and that supports physiological full flexion/extension of the fingers while offering an effortless setup. Our palmar design, based on a parallelogram coupled to a principal revolute joint, introduces the following novelties: (1) While allowing for an effortless installation of the user's hand, it offers large range of motion of the fingers (full extension to 180° flexion). (2) The kinematic design ensures that all fingers are supported through the full range of motion and that the little finger does not lose contact with the finger support in extension. (3) We took into consideration that a handle is usually comfortably grasped such that its longitudinal axis runs obliquely from the metacarpophalangeal joint of the index finger to the base of the hypothenar eminence. (4) The fingertip path was optimized to guarantee physiologically correct finger movements for a large variety of hand sizes. Moreover, the device possesses a high mechanical transparency, which was achieved using a backdrivable cable transmission. The transparency was further improved with the implementation of friction and gravity compensation. In a test with six healthy participants, the root mean square of the human-robot interaction force was found to remain as low as 1.37 N in a dynamic task. With its clinical-driven design and easy-to-use setup, our robotic device for hand sensorimotor rehabilitation has the potential for high clinical acceptance, applicability and effectiveness.

The Influence of the Abduction Joints of Four Fingers to Grasp: Experimental and Simulated Verification

The thumb is the most important finger of the human hand and has a great influence on grasp manipulations. However, the extent to which joints other than the thumb joints affect the grasp, and thus, which joints should be included in a prosthetic hand, remains an open issue. In this paper, we focus on the metacarpophalangeal joints of the four fingers, except the thumb, which can generate flexion/extension and abduction/adduction motions. The contribution of these joints to grasping was evaluated in four aspects: grasp size, grasp force, grasp quality and grasp success rate. Six subjects participated in experiments with respect to the maximum grasp size and grasp force. The results show that possessing abduction mobility of the metacarpophalangeal joints can increase the grasp size by 4.67 ± 1.93 mm and the grasp force by 5.27 ± 4.25 N. Then, the grasping quality and success rate were tested in a simulation platform and using a robotic hand, respectively. The results show that grasp quality was promoted by 76.7% in the simulated environment with abduction mobility compared to without abduction mobility, whereas the grasp success rate was promoted by 68.3%. We believe that the results of this work can benefit the understanding of hand function and prosthetic hand design.

Integrated linkage-driven dexterous anthropomorphic robotic hand

Robotic hands perform several amazing functions similar to the human hands, thereby offering high flexibility in terms of the tasks performed. However, developing integrated hands without additional actuation parts while maintaining important functions such as human-level dexterity and grasping force is challenging. The actuation parts make it difficult to integrate these hands into existing robotic arms, thus limiting their applicability. Based on a linkage-driven mechanism, an integrated linkage-driven dexterous anthropomorphic robotic hand called ILDA hand, which integrates all the components required for actuation and sensing and possesses high dexterity, is developed. It has the following features: 15-degree-of-freedom (20 joints), a fingertip force of 34N, compact size (maximum length: 218 mm) without additional parts, low weight of 1.1 kg, and tactile sensing capabilities. Actual manipulation tasks involving tools used in everyday life are performed with the hand mounted on a commercial robot arm.

Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study

One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, combining pre-trained models (obtained from prior subjects) with training sessions performed on a specific user. Although a few promising results were reported in the past, it was recently shown that the use of conventional transfer learning algorithms does not increase performance if proper hyperparameter optimization is performed on the standard approach that does not exploit transfer learning. The objective of this paper is to introduce novel analyses on this topic by using a random forest classifier without hyperparameter optimization and to extend them with experiments performed on data recorded from the same patient, but in different data acquisition sessions. Two domain adaptation techniques were tested on the random forest classifier, allowing us to conduct experiments on healthy subjects and amputees. Differently from several previous papers, our results show that there are no appreciable improvements in terms of accuracy, regardless of the transfer learning techniques tested. The lack of adaptive learning is also demonstrated for the first time in an intra-subject experimental setting when using as a source ten data acquisitions recorded from the same subject but on five different days.