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In conjunction with sustainable development goal 3 (SDG 3), it is important to develop a national electrical component for physiotherapy robot development. This study aimed to develop an open-loop field-oriented control (FOC) driver utilizing BTS7960. The driver utilized three BTS7960s that produce sinewave with variable angular frequency (ω). The research then compared the open-loop FOC driver with electronics speed controller (ESC) performance to drive a brushless DC (BLDC) motor with an initial rotation per minute (RPM) of 400, 500, and 600. The main observation was RPM reduction when the BLDC motor was subjected to loads of 20, 35, 50, 65, and 80 gr. The result showed that the open-loop FOC driver performed better, especially on an 80 gr load. For an initial RPM of 600, the RPM reduced to 100 when controlled with an open-loop FOC driver, but lesser when controlled using ESC. The open-loop FOC driver produces higher torque on the BLDC motor so it could rotate with less reduction compared to ESC, which is evident. The open-loop FOC driver can be easily developed using BTS7960 with a settling time of 4 seconds. However future studies should still consider close-loop FOC drivers to achieve higher torque performance and faster transient response for physiotherapy robot applications.

Physiotherapy robots offer a feasible and promising solution for achieving safe and efficient treatment. Among these, acupoint recognition is the core component that ensures the precision of physiotherapy robots. Although the research on the acupoint recognition such as hand and ear has been extensive, the accurate location of acupoints on the back of the human body still faces great challenges due to the lack of significant external features. This paper designs a two-stage acupoint recognition method, which is achieved through the cooperation of two detection networks. First, a lightweight RTMDet network is used to extract the effective back range from the image, and then the acupoint coordinates are inferred from the extracted back range, reducing the inference consumption caused by invalid information. In addition, the RTMPose network based on the SimCC framework converts the acupoint coordinate regression problem into a classification problem of sub-pixel block subregions on the X and Y axes by performing sub-pixel-level segmentation of images, significantly improving detection speed and accuracy. Meanwhile, the multi-layer feature fusion of CSPNeXt enhances feature extraction capabilities. Then, we designed a physiotherapy interaction interface. Through the three-dimensional coordinates of the acupoints, we independently planned the physiotherapy task path of the physiotherapy robot. We conducted performance tests on the acupoint recognition system and physiotherapy task planning in the physiotherapy robot system. The experiments have proven our effectiveness, achieving a recall of 90.17% on human datasets, with a detection error of around 5.78 mm. At the same time, it can accurately identify different back postures and achieve an inference speed of 30 FPS on a 4070Ti GPU. Finally, we conducted continuous physiotherapy tasks on multiple acupoints for the user. The experimental results demonstrate the significant advantages and broad application potential of this method in improving the accuracy and reliability of autonomous acupoint recognition by physiotherapy robots.

The high reliance on traditional Chinese medicine (TCM) knowledge and practical skills has led to prolonged training cycles for therapists and a significant talent shortage, posing a severe challenge to the standardization of TCM physiotherapy in rehabilitation. Meanwhile, most physiotherapy robots based on rigid robotics technology suffer from insufficient safety and adaptability, resulting in unsatisfactory treatment effects. This paper presents a novel wearable pneumatic soft physiotherapy device, designed to achieve high‐safety and personalized physiotherapy for shoulder and neck acupoint massage. The device integrates four soft massage actuators, four embedded thin‐film stress sensors, two miniature airflow sources, and a power supply system, enhancing its portability. Additionally, a hybrid control strategy combining feedforward inverse compensation and PID feedback is proposed to regulate and customize the therapy intensity on human acupoints. Experimental results demonstrate that the developed device can apply therapeutic intensities ranging from 0 to 3.45 N to human acupoints, effectively adapting to the human shoulder and neck region to achieve good wearing comfort, and performing safe acupoint massage with customized physiotherapy intensities. This study explores and realizes the integration of emerging soft robotics technology with TCM, providing a paradigm for the development of more soft physiotherapy devices. A finger‐inspired wearable pneumatic soft physiotherapy device integrates embedded miniature pneumatic systems, flexible bionic thumbs and forefingers capable of mimicking the motions of human fingers such as bending and straightening. It replicates traditional kneading and pointing massage techniques for acupoint stimulation, providing portable, comfortable, and effective shoulder and neck therapy based on traditional Chinese medicine principles.

This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, in order to predict the future joint trajectories of the robot. The proposed framework also uses a Segment Online Dynamic Time Warping (SODTW) algorithm to quantify the closeness between the robot and patient motion. The SODTW cost decides the amount of modification needed in the inputs to our deep RNN network, which in turn adapts the robot movements. By keeping the prediction mechanism (RNN) and adaptation mechanism (SODTW) separate, the framework achieves modularity, flexibility, and scalability. We tried both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) RNN architectures within our proposed framework. Experiments involved a group of 15 human subjects performing a range of motion tasks in conjunction with our social robot, Zeno. Comparative analysis of the results demonstrated the superior performance of the LSTM RNN across multiple task variations, highlighting its enhanced capability for adaptive motion imitation.

This paper presents the mechanical architecture, control system, and other modules of a physiotherapy service robot which can treat degenerative disease and chronic disease of middle-aged and aged people by Chinese massage skill. The main body of the robot includes a massage adjustable bed, two 4-DOF(Degree of Freedom) robot arms and two massage hands that can accomplish various massage manipulations. Physiological signal and massage pressure are detecting in real time in massage process to ensure a scientific and safe therapy. Vision system sends the recognized acupoint position to the master system to track the patient’s body, and the acupoint being massaged is displayed in real time by the 3D virtual model. The robot can execute ten massage manipulations, so that the traditional Chinese massage can have a robot instead. The effectiveness for degenerative lumbago in middle-aged and aged is demonstrated by laboratory examination and clinical trial.

The aim of this study was to compare the effectiveness of traditional neurological rehabilitation and neurological rehabilitation combined with a rehabilitation robot for patients with post-COVID-19 fatigue syndrome. Eighty-six participants transferred from intensive care units due to post-viral fatigue after COVID-19 were randomly divided into two groups: the intervention group and the control group. The control group received standard neurological rehabilitation for 120 min a day, while the intervention group received the same neurological rehabilitation for 75 min a day, complemented by 45 min of exercises on the rehabilitation robot. The Berg scale, Tinetti scale, six-minute walking test, isokinetic muscle force test, hand grip strength, Barthel Index, and Functional Independence Measure were used to measure the outcomes. Both groups improved similarly during the rehabilitation. Between groups, a comparison of before/after changes revealed that the intervention group improved better in terms of Functional Independence Measure (p = 0.015) and mean extensor strength (p = 0.023). The use of EMG-driven robots in the rehabilitation of post-COVID-19 fatigue syndrome patients was shown to be effective.

Abstract—Robotic technologies offer a range of functions to augment clinical rehabilitation practice. However, compliance with robot-assisted rehabilitation techniques has not been optimally achieved. Traditional approaches to improving the treatment efficacy are focusing more on the system function, while psychological factors have not been integrated comprehensively. In this perspective paper, 8 key factors reflecting three conceptions-robot design, function design, and patients’ expectations have been evaluated and analyzed. Clinical results with 28 therapists and 84 patients indicate that integrating psychological strategies into robot-assisted physiotherapy may promote better trust and acceptance of rehabilitation robots.

Socially assistive robots (SARs) are robotic technology platforms equipped with sensing (eg, through audio or visual) and acting (eg, speech and movement) capabilities to interact socially with users. SARs are increasingly adopted in physiotherapy to aid patients in their rehabilitation journey by providing feedback, motivation, and encouragement. However, while many studies have explored SAR implementation in physiotherapy, research involving clinical populations remains scarce, and the overall state of SAR deployment is unclear. This scoping review aimed to explore the use of SARs in physiotherapy with clinical populations, how the effectiveness of these interventions has been evaluated, and identify limitations and new areas of application and future work. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Review) reporting guideline extension, comprehensive searches based on SARs and physiotherapy were conducted in various databases. Title and abstract screening were performed by 1 reviewer, with full-text screening conducted by 2 reviewers. Data extraction, synthesis, and analysis were completed by 1 reviewer. Data on SAR roles were categorized and synthesized using content analysis. Other descriptive texts were summarized to improve readability. Our findings suggest that SARs are commonly used in rehabilitation clinics and hospital inpatient settings, primarily for neurological conditions. In these interventions, SARs typically serve roles, such as coaching, demonstration, monitoring, and peer support. Their effectiveness is generally evaluated through clinical outcomes, user performance, functional measures, and metrics assessing the robots' acceptability, usability, and perception. This scoping review highlighted SARs' potential to address challenges faced by human therapists due to the demands of time-extending coaching and monitoring and the limited availability of therapists. Future research should focus on addressing the limitations identified in this scoping review, including small sample sizes, technical issues in both the robot and intervention design, sufficient involvement of key stakeholders in the design and development of SAR-based interventions, and conducting more clinical trials to investigate SAR intervention effectiveness.

Joint position sense (JPS) is the awareness of joint location in space, indicating accuracy and precision of the movement. Therefore, the aim of the present study is to determine the reliability of active and passive JPS assessment regarding the knee joint. This was carried out using the Luna EMG rehabilitation robot. Further analysis assessed whether the examination of only the dominant site is justified and if there are differences between sites. The study comprised 24 healthy male participants aged 24.13 ± 2.82 years, performing sports at a recreational level. Using the Luna EMG rehabilitation robot, JPS tests were performed for the right and left knees during flexion and extension in active and passive mode, in two separate sessions with a 1-week interval. Both knee flexion and extension in active and passive modes demonstrated high reliability (ICC = 0.866–0.982; SEM = 0.63–0.31). The mean JPS angle error did not differ significantly between the right and left lower limbs (p < 0.05); however, no between-limb correlation was noted (r = 0.21–0.34; p > 0.05). The Bland–Altman plots showed that the between-limb bias was minimal, with relatively wide limits of agreement. Therefore, it was concluded that the Luna EMG rehabilitation robot is a reliable tool for active and passive knee JPS assessment. In our study, JPS angle error did not differ significantly between left and right sides; however, the slight asymmetry was observed (visible in broad level of agreement exceeding 5° in Bland–Altman plots), what may suggest that in healthy subjects, e.g., active athletes, proprioception should always be assessed on both sides.

Hand impairment, followed by stroke, causes significant deficits in performing different activities. Restoration of hand functions requires regular and repetitive therapy exercises. Although robotically physiotherapy systems have shown great promise in hand functions' improvements, they are not widely and effectively used, as the needs and expectations of patients and physiotherapists have been ignored. In this paper, a 4-degrees of freedom desktop-mounted robot is developed for four fingers and wrist rehabilitation, based on clinical observations. Hand-Robo-Hab provides the four mechanotherapy prevalent movements as follows: active, passive, active-assisted, and active resisted. In this study, the design and development of the robot are described. Then the efficiency and usability of the device are evaluated through two technical tests, and a preliminary clinical trial. The results of technical tests showed that Hand-Robo-Hab was able to cover the wrist range of motion of 99 to 203 degrees. Besides, the proposed device was capable of compensating its weight, which is a necessary step to accomplish the active modes exercises. In addition, clinical trial results showed that Hand-Robo-Hab was both operative and comfortable for patients with different hand sizes.