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The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test.

To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details.

The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests.

A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.

This study aims to evaluate the enhancement in prosthetic supply chain capabilities resulting from the implementation of additive manufacturing (AM) technologies. The study presents an emerging model outlining the key areas that undergo changes when integrating 3D printing technologies into the prosthetic supply chain.

Employing a qualitative approach, data were collected through field observations and 31 in-depth interviews conducted within various Jordanian organizations associated with the prosthetic industry and 3D printing technologies.

The findings suggest that the adoption of 3D printing technologies improves the prosthetic supply chain’s capabilities in terms of customization, responsiveness, innovation, environmental sustainability, cost minimization and patient empowerment. The study sheds light on the specific areas affected in the prosthetic supply chain following the adoption of 3D printing technologies, emphasizing the overall improvement in supply chain capabilities within the prosthetic industry.

This study provides recommendations for governmental bodies and prosthetic organizations to maximize the benefits derived from the use of 3D printing technologies.

This study contributes as the first of its kind in exploring the impact of 3D printing technology adoption in the Jordanian prosthetic industry, elucidating the effects on the supply chain and identifying challenges for decision-makers in an emerging market context.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

This study aims to examine the relationship between neutrophil to lymphocyte ratio (NLR), handgrip strength (HGS) and nutritional status in non-critically ill patients and outpatients.

A cross-sectional study was conducted on 80 geriatric patients. Nutritional status was evaluated using the Mini Nutritional Assessment Short Form, Geriatric Nutritional Risk Index, some anthropometric and biochemical parameters. NLR was calculated from the complete blood count results. Data were analyzed using SPSS 24.0.

While 38.3% of patients were malnourished or at risk of malnutrition, 61.7% had normal nutritional status. Mini Nutritional Assessment Short Form was not correlated with NLR, whereas it showed a weak positive correlation with HGS. According to the logistic regression analysis, age, HGS, hemoglobin and platelet to lymphocyte ratio were significant independent factors for predicting malnutrition or risk of malnutrition. The receiver operator characteristic curve analysis showed that the optimum HGS cut-off point for patients with malnourished or at risk of malnutrition was 13.2. In conclusion, HGS was associated with the nutritional status. NLR was not associated with nutritional status but associated with nutritional risk.

It is well known that malnutrition is a serious health problem among older adults, and it is important to assess the nutritional status of older adults because of the adverse health effects. In addition, to the best of the authors’ knowledge, this is the first study to determine the relationship between NLR, HGS and nutritional status in non-critically ill patients and outpatients.

This study aims to conduct text mining and semantic network analysis of muscle-supportive and posture-corrective wearable robots for the elderly to understand key terms related to the topic and to identify considerations for developing these types of clothing.

The authors searched and identified the key terms wearable robot, muscle-supportive, posture correction and elderly using the text-mining software Textom to extract terms as well as the network analysis software UCINET 6 to process and visualize the relationships among the terms. The authors compared and analyzed the term frequency (TF), the TF-inverse document frequency and the degree centrality of the terms, and the authors visualized and summarized the terms using NetDraw.

The key terms and their relationships in 3–4 groups were identified: wearable robot, muscle-supportive, posture correction and elderly. The authors identified the aspects of designing muscle-supportive and posture-corrective wearable robots for the elderly.

This study contributes to the field of muscle-supportive clothing and wearable robotics by deriving insights into what people are discussing and interested in, and by offering recommendations when developing these types of clothing for the elderly.

Biofeedback is used for regulating vestibular adaptation and balance by providing real-time stimulus to the individual during physical activities. This study aimed at (1) developing a wearable device, which tracks balance, counts the number and the direction of balance losses and provides haptic biofeedback through real-time vibration stimulus (2) investigating device efficacy on an adolescent medulloblastoma patient during static and dynamic tasks.

A 16-year-old medulloblastoma patient used the device during 10-m walking and single-leg stance tests. The knee joint kinematics and the number and direction of balance losses were recorded for “with” and “without” biofeedback conditions.

The device helped regulate the knee joint kinematics and reduce the number of balance losses of the medulloblastoma patient. The knee joint movement pattern similarity of the control subject was highly correlated (R² = 0.997, RMSE = 1.232). Conversely, medulloblastoma patient knee joint movement pattern similarity was relatively weak (R² = 0.359, RMSE = 18.6) when “with” and “without” biofeedback conditions were compared. The number of balance losses decreased when the medulloblastoma patient was guided with biofeedback.

The major limitation of this pilot study is the lack of a large and homogeneous number of participants. The medulloblastoma patient used the device while walking after she was given enough time to get used to the tactile biological feedback, so the long-term effect of the device and biofeedback guidance were not investigated. Additionally, the potential desensitization with prolonged use of the device was not evaluated.

Biofeedback reduced the number of balance losses; additionally, the knee joint movement pattern was regulated during static and dynamic tasks. This device can be integrated into the physical therapy of patients with balance, vestibular and postural control impairments.

This is compact and has an easy-to-wear design, patients, who have balance and postural control impairments, can practically use the device during their activities of daily living.

The device promotes physical activity adaptation and regulates gait through continuous and real-time balance control. Its design makes it simple for the user to wear it beneath clothing while using the sensor.

Pervasive analytics act as a prominent role in computer-aided prediction of non-communicating diseases. In the early stage, arrhythmia diagnosis detection helps prevent the cause of death suddenly owing to heart failure or heart stroke. The arrhythmia scope can be identified by electrocardiogram (ECG) report.

The ECG report has been used extensively by several clinical experts. However, diagnosis accuracy has been dependent on clinical experience. For the prediction methods of computer-aided heart disease, both accuracy and sensitivity metrics play a remarkable part. Hence, the existing research contributions have optimized the machine-learning approaches to have a great significance in computer-aided methods, which perform predictive analysis of arrhythmia detection.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

In reference to this, this paper determined a regression heuristics by tridimensional optimum features of ECG reports to perform pervasive analytics for computer-aided arrhythmia prediction. The intent of these reports is arrhythmia detection. From an empirical outcome, it has been envisioned that the project model of this contribution is more optimal and added a more advantage when compared to existing or contemporary approaches.

This paper aims to understand the clinical practice of physiotherapists and occupational therapists in the Republic of Ireland in the assessment and treatment of spasticity in adults, to inform and improve spasticity management practice. This study also aims to describe therapists’ knowledge, confidence and perceived barriers in the management of spasticity.

A cross sectional survey design study was completed, and respondents were recruited through an online survey.

In total, 92 respondents from a wide range of clinical settings revealed there is considerable variation in services available nationally for adults presenting with spasticity. There were significant inconsistencies across all areas of practice. The majority of respondents (94%) did provide intervention to patients with spasticity, yet three quarters did not have access to a specialist spasticity clinic, and the majority (82%) did not feel they were providing sufficient treatment intensity for spasticity.

These findings provide a unique insight into the assessment and treatment practices of Irish physiotherapists and occupational therapists. These results demonstrate the need for further upskilling and specialist high-quality spasticity services nationally.