Mechanomyography reflects the changes in oxygenated hemoglobin during electrically evoked cycling in individuals with spinal cord injury.

BACKGROUND: Functional electrical stimulation (FES) cycling has been reported to enhance muscle strength and improve muscle fatigue resistance after spinal cord injury (SCI). Despite its proposed benefits, the quantification of muscle fatigue during FES cycling remains poorly documented. This study sought to quantify the relationship between the vibrational performance of electrically-evoked muscles measured through mechanomyography (MMG) and its oxidative metabolism through near-infrared spectroscopy (NIRS) characteristics during FES cycling in fatiguing paralyzed muscles in individuals with SCI. METHODS: Six individuals with SCI participated in the study. They performed 30 min of FES cycling with MMG and NIRS sensors on their quadriceps throughout the cycling, and the signals were analyzed. RESULTS: A moderate negative correlation was found between MMG root mean square (RMS) and oxyhaemoglobin (O2Hb) [r = -0.38, p = 0.003], and between MMG RMS and total hemoglobin (tHb) saturation [r = -0.31, p = 0.017]. Statistically significant differences in MMG RMS, O2Hb, and tHb saturation occurred during pre- and post-fatigue of FES cycling (p < 0.05). CONCLUSIONS: MMG RMS was negatively associated with O2Hb and muscle oxygen derived from NIRS. MMG and NIRS sensors showed good inter-correlations, suggesting a promising use of MMG for characterizing metabolic fatigue at the muscle oxygenation level during FES cycling in individuals with SCI.

Biomechanics principle of elbow joint for transhumeral prostheses: comparison of normal hand, body-powered, myoelectric & air splint prostheses.

BACKGROUND: Understanding of kinematics force applied at the elbow is important in many fields, including biomechanics, biomedical engineering and rehabilitation. This paper provides a comparison of a mathematical model of elbow joint using three different types of prosthetics for transhumeral user, and characterizes the forces required to overcome the passive mechanical of the prosthetics at the residual limb. METHODS: The study modeled the elbow as a universal joint with intersecting axes of x-axis and y-axis in a plain of upper arm and lower arm. The equations of force applied, torque, weight and length of different type of prosthetics and the anthropometry of prosthetics hand are discussed in this study. The study also compares the force, torque and pressure while using all three types of prosthetics with the normal hand. RESULTS: The result was measured from the elbow kinematics of seven amputees, using three different types of prosthetics. The F-Scan sensor used in the study is to determine the pressure applied at the residual limb while wearing different type of prostheses. CONCLUSION: These technological advances in assessment the biomechanics of an elbow joint for three different type of prosthetics with the normal hand bring the new information for the amputees and prosthetist to choose the most suitable device to be worn daily.

Development and performance of a new prosthesis system using ultrasonic sensor for wrist movements: a preliminary study.

BACKGROUND: The design and performance of a new development prosthesis system known as biomechatronics wrist prosthesis is presented in this paper. The prosthesis system was implemented by replacing the Bowden tension cable of body powered prosthesis system using two ultrasonic sensors, two servo motors and microcontroller inside the prosthesis hand for transradial user. METHODS: The system components and hand prototypes involve the anthropometry, CAD design and prototyping, biomechatronics engineering together with the prosthetics. The modeler construction of the system develop allows the ultrasonic sensors that are placed on the shoulder to generate the wrist movement of the prosthesis. The kinematics of wrist movement, which are the pronation/supination and flexion/extension were tested using the motion analysis and general motion of human hand were compared. The study also evaluated the require degree of detection for the input of the ultrasonic sensor to generate the wrist movements. RESULTS: The values collected by the vicon motion analysis for biomechatronics prosthesis system were reliable to do the common tasks in daily life. The degree of the head needed to bend to give the full input wave was about 45°-55° of rotation or about 14 cm-16 cm. The biomechatronics wrist prosthesis gave higher degree of rotation to do the daily tasks but did not achieve the maximum degree of rotation. CONCLUSION: The new development of using sensor and actuator in generating the wrist movements will be interesting for used list in medicine, robotics technology, rehabilitations, prosthetics and orthotics.

Clinical implication of interface pressure for a new prosthetic suspension system.

BACKGROUND: Prosthesis suspension systems can alter the distribution of pressure within the prosthetic socket. This study evaluates a new suspension system for lower limb prostheses, and aims to compare the interface pressure and amputees' satisfaction with the new system compared with a common prosthetic suspension system (pin/lock). METHODS: Ten transtibial amputees walked at a self-selected speed on a level ground with two different suspension systems, namely the pin/lock and HOLO system. The interface pressure was measured using the F-socket transducers at the proximal, middle and distal sites of residual limb. Furthermore, subjective feedback was logged to compare two systems. RESULTS: The pressure was significantly higher at the proximal and distal areas with the pin/lock suspension system during the swing phase of gait (P<0.05). Subjective feedback also showed traction at the stump with the pin/lock system. There were no significant differences in the pressure applied to the mid-anterior and mid posterior stump for both suspension systems. However, the lateral and medial sides exhibited higher pressure with the new system during stance phase. CONCLUSIONS: The intention of this study was to deepen understanding on the effect of suspension system on the load distribution over the residual limb. The new coupling system was proved compatible with the pin/lock system in terms of suspending the leg and amputee's satisfaction. On the other hand, the HOLO system could distribute the pressure more uniformly over the residual limb.

Effects of releasing ankle joint during electrically evoked cycling in persons with motor complete spinal cord injury.

Literature has shown that simulated power production during conventional functional electrical stimulation (FES) cycling was improved by 14% by releasing the ankle joint from a fixed ankle setup and with the stimulation of the tibialis anterior and triceps surae. This study aims to investigate the effect of releasing the ankle joint on the pedal power production during FES cycling in persons with spinal cord injury (SCI). Seven persons with motor complete SCI participated in this study. All participants performed 1 min of fixed-ankle and 1 min of free-ankle FES cycling with two stimulation modes. In mode 1 participants performed FES-evoked cycling with the stimulation of quadriceps and hamstring muscles only (QH stimulation), while Mode 2 had stimulation of quadriceps, hamstring, tibialis anterior, and triceps surae muscles (QHT stimulation). The order of each trial was randomized in each participant. Free-ankle FES cycling offered greater ankle plantar- and dorsiflexion movement at specific slices of 20° crank angle intervals compared to fixed-ankle. There were significant differences in the mean and peak normalized pedal power outputs (POs) [F(1,500) = 14.03, p < 0.01 and F(1,500) = 7.111, p = 0.008, respectively] between fixed- and free-ankle QH stimulation, and fixed- and free-ankle QHT stimulation. Fixed-ankle QHT stimulation elevated the peak normalized pedal PO by 14.5% more than free-ankle QH stimulation. Releasing the ankle joint while providing no stimulation to the triceps surae and tibialis anterior reduces power output. The findings of this study suggest that QHT stimulation is necessary during free-ankle FES cycling to maintain power production as fixed-ankle.

Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.

Background: This study introduced a novel approach for predicting occupational injury severity by leveraging deep learning-based text classification techniques to analyze unstructured narratives. Unlike conventional methods that rely on structured data, our approach recognizes the richness of information within injury narrative descriptions with the aim of extracting valuable insights for improved occupational injury severity assessment. Methods: Natural language processing (NLP) techniques were harnessed to preprocess the occupational injury narratives obtained from the US Occupational Safety and Health Administration (OSHA) from January 2015 to June 2023. The methodology involved meticulous preprocessing of textual narratives to standardize text and eliminate noise, followed by the innovative integration of Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) word embeddings for effective text representation. The proposed predictive model adopts a novel Bidirectional Long Short-Term Memory (Bi-LSTM) architecture and is further refined through model optimization, including random search hyperparameters and in-depth feature importance analysis. The optimized Bi-LSTM model has been compared and validated against other machine learning classifiers which are naïve Bayes, support vector machine, random forest, decision trees, and K-nearest neighbor. Results: The proposed optimized Bi-LSTM models' superior predictability, boasted an accuracy of 0.95 for hospitalization and 0.98 for amputation cases with faster model processing times. Interestingly, the feature importance analysis revealed predictive keywords related to the causal factors of occupational injuries thereby providing valuable insights to enhance model interpretability. Conclusion: Our proposed optimized Bi-LSTM model offers safety and health practitioners an effective tool to empower workplace safety proactive measures, thereby contributing to business productivity and sustainability. This study lays the foundation for further exploration of predictive analytics in the occupational safety and health domain.

Children's Physiological and Perceptual Responses to Sports Exergames When Played in Different Positions.

Today's children are prone to becoming involved in exergames, but their positions during play have not been sufficiently investigated to determine whether the positions they adopt result in equal responses. The design of this study involved the collection of physiological and perceptual responses (i.e., heart rate (HR), rating of perceived exertion, and enjoyment score) during exergames in three different sports (bowling, tennis, and boxing) with players in different positions (sitting and standing). The participants played each game for 10 min while their HR was recorded. After the gameplay, each perceptual response was retrieved. The results revealed a significant increase in HR above rest during exergaming overall (p < 0.001). Standing gameplay resulted in a significantly higher HR (p < 0.001) than seated gameplay. Compared to tennis and bowling, boxing produced the highest physiological response (p < 0.001) and perceived exertion (p < 0.05) in both positions. The participants perceived all the sports exergames to be enjoyable, as their enjoyment scores did not significantly differ for each game (p > 0.5). For all the variables, no statistically significant differences between genders were identified (p > 0.5). This home-based intervention demonstrated that sports exergames are not only enjoyable; overall, they can provide at least moderately intense physical activity, whether played seated or standing.

A systematic review of methods used to assist transtibial prosthetic alignment decision-making.

Prosthetic alignment is a highly subjective process that is still based on clinical judgments. Thus, researchers have aimed their effort to quantify prosthetic alignment by providing an objective method that can assist and guide prosthetists in achieving transtibial (TT) prosthetic alignment. This systematic review aimed to examine the current literature on TT prosthetic alignment to scope the qualitative and quantitative methods designed to guide prosthetists throughout the TT prosthetic alignment process as well as evaluate the reported instruments and devices that are used to align TT prostheses and their clinical feasibility. A literature search, completed in June 2022, was performed using the following databases: Web of Science (Clarivate), SCOPUS (Elsevier), and Pub Med (Medline) with searching terms focusing on TT, prosthesis, prosthetist, prosthetic alignment, and questionnaires, resulting in 2790 studies being screened. Twenty-four studies have used quantitative methodologies, where sensor technologies were found to be the most frequently proposed technology combined with gait analysis tools and/or subjective assessments. A qualitative method that assists prosthetists throughout the alignment process was not found. In this systematic review, we presented diverse methods for guiding and assisting clinical decision-making regarding TT prosthetic alignment. However, most of these methods considered varied parameters, and there is a need for elaboration toward standardized methods, which would improve the prosthetic alignment clinical outcome.

Critical Device Reliability Assessment in Healthcare Services.

Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

Surface profile of laminated transfemoral socket fabricated with different types of reinforcement materials.

Composite materials used in the prosthetic and orthotic fields have helped improve the fabrication of sockets. Laminated sockets proved to be stronger than conventional thermoplastic sockets. The internal surface of a laminated socket plays an important role in patient comfort and is influenced by the material used to fabricate the socket. This study analyzes the internal surface profile of five different materials, that is, Dacron felt, fiberglass, Perlon stockinette, polyester stockinette, and elastic stockinette. All sockets were fabricated using an acrylic resin mix with hardener powder at a ratio of 100:3. The internal surface of the sockets was tested using the Mitutoyo SurfTest SJ-210 series for 20 trials. The overall Ra values were 2.318, 2.380, 2.682, 2.722, and 3.750 µm for fiberglass, polyester, Perlon, elastic stockinette, and Dacron felt. Dacron felt yielded the lowest Ra value, thus, producing the smoothest internal surface but requiring high skill and the correct technique during the fabrication of a laminated socket. Fiberglass is considered the best material for the internal surface despite not producing the lowest value individually but overall is the lowest and most consistent, indicating that it is easy to use to laminate prosthetic sockets.

Effect of Infill Parameters on the Compressive Strength of 3D-Printed Nylon-Based Material.

3D printing is the most suitable method to manufacture the frame parts of powered ankle-foot prostheses but the compressive strength of the 3D-printed part needs to be ensured. According to the compression test standard ASTM D695, the effect of infill pattern and density, which is transferred to the mass of the standard specimen, on the compressive strength is investigated with a carbon fiber-reinforced nylon material. With the same infill pattern, specimens with more mass have a higher compressive strength. With the same mass, specimens with triangular fill have a higher compressive strength than those with rectangular and gyroid fills. Compared with specimens with a solid fill, specimens with a triangular fill can also provide more compressive strength in a unit mass. According to the results of standard specimens, following the requirement of strength and lightweight, 41% triangular fill is selected to manufacture the supporting part of a powered ankle-foot prosthesis. Under a compressive load of 1225 N, the strain of the assembly of the standard adaptor and the 3D-printed part is 1.32 ± 0.04%, which can meet the requirement of the design. This study can provide evidence for other 3D-printed applications with the requirement of compressive strength.

Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management.

Background: The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services. Methods: Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed. Results: This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author's future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices' maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.

Contributions of the Cybathlon championship to the literature on functional electrical stimulation cycling among individuals with spinal cord injury: A bibliometric review.

BACKGROUND: Due to its clinically proven safety and health benefits, functional electrical stimulation (FES) cycling has become a popular exercise modality for individuals with spinal cord injury (SCI). Since its inception in 2013, the Cybathlon championship has been a platform for publicizing the potential of FES cycling in rehabilitation and exercise for individuals with SCI. This study aimed to evaluate the contribution of the Cybathlon championship to the literature on FES cycling for individuals with SCI 3 years pre and post the staging of the Cybathlon championship in 2016. METHODS: Web of Science, Scopus, ScienceDirect, IEEE Xplore, and Google Scholar databases were searched for relevant studies published between January 2013 and July 2019. The quality of the included studies was objectively evaluated using the Downs and Black checklist. RESULTS: A total of 129 articles on FES cycling were retained for analysis. A total of 51 articles related to Cybathlon were reviewed, and 14 articles were ultimately evaluated for the quality. In 2017, the year following the Cybathlon championship, Web of Science cited 23 published studies on the championship, which was almost 5-fold more than that in 2016 (n = 5). Training was most often reported as a topic of interest in these studies, which mostly (76.7%) highlighted the training parameters of interest to participating teams in their effort to maximize their FES cycling performance during the Cybathlon championship. CONCLUSION: The present study indicates that the Cybathlon championship in 2016 contributed to the number of literature published in 2017 on FES cycling for individuals with SCI. This finding may contribute to the lessons that can be learned from participation in the Cybathlon and potentially provide additional insights into research in the field of race-based FES cycling.

Stiffness estimation of planar spiral spring based on Gaussian process regression.

Planar spiral spring is important for the dimensional miniaturisation of motor-based elastic actuators. However, when the stiffness calculation of the spring arm is based on simple beam bending theory, the results possess substantial errors compared with the stiffness obtained from finite-element analysis (FEA). It deems that the errors arise from the spiral length term in the calculation formula. Two Gaussian process regression models are trained to amend this term in the stiffness calculation of spring arm and complete spring. For the former, 216 spring arms' data sets, including different spiral radiuses, pitches, wrap angles and the stiffness from FEA, are employed for training. The latter engages 180 double-arm springs' data sets, including widths instead of wrap angles. The simulation of five spring arms and five planar spiral springs with arbitrary dimensional parameters verifies that the absolute values of errors between the predicted stiffness and the stiffness from FEA are reduced to be less than 0.5% and 2.8%, respectively. A planar spiral spring for a powered ankle-foot prosthesis is designed and manufactured to verify further, of which the predicted value possesses a 3.25% error compared with the measured stiffness. Therefore, the amendment based on the prediction of trained models is available.

Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance.

Forecasting the severity of occupational injuries shall be all industries' top priority. The use of machine learning is theoretically valuable to assist the predictive analysis, thus, this study attempts to propose a feature-optimized predictive model for anticipating occupational injury severity. A public database of 66,405 occupational injury records from OSHA is analyzed using five sets of machine learning models: Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. For model comparison, Random Forest outperformed other models with higher accuracy and F1-score. Therefore, it highlighted the potential of ensemble learning as a more accurate prediction model in the field of occupational injury. In constructing the model, this study also proposed the feature optimization technique that revealed the three most important features; 'nature of injury', 'type of event', and 'affected body part' in developing model. The accuracy of the Random Forest model was improved by 0.5% or 0.895 and 0.954 for the prediction of hospitalization and amputation, respectively by redeveloping and optimizing the model with hyperparameter tuning. The feature optimization is essential in providing insight knowledge to the Safety and Health Practitioners for future injury corrective and preventive strategies. This study has shown promising potential for smart workplace surveillance.

Predicting occupational injury causal factors using text-based analytics: A systematic review.

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.

Machine Learning Application of Transcranial Motor-Evoked Potential to Predict Positive Functional Outcomes of Patients.

Intraoperative neuromonitoring (IONM) has been used to help monitor the integrity of the nervous system during spine surgery. Transcranial motor-evoked potential (TcMEP) has been used lately for lower lumbar surgery to prevent nerve root injuries and also to predict positive functional outcomes of patients. There were a number of studies that proved that the TcMEP signal's improvement is significant towards positive functional outcomes of patients. In this paper, we explored the possibilities of using a machine learning approach to TcMEP signal to predict positive functional outcomes of patients. With 55 patients who underwent various types of lumbar surgeries, the data were divided into 70 : 30 and 80 : 20 ratios for training and testing of the machine learning models. The highest sensitivity and specificity were achieved by Fine KNN of 80 : 20 ratio with 87.5% and 33.33%, respectively. In the meantime, we also tested the existing improvement criteria presented in the literature, and 50% of TcMEP improvement criteria achieved 83.33% sensitivity and 75% specificity. But the rigidness of this threshold method proved unreliable in this study when different datasets were used as the sensitivity and specificity dropped. The proposed method by using machine learning has more room to advance with a larger dataset and various signals' features to choose from.

Physiological Responses of Exergaming Boxing in Adults: A Systematic Review and Meta-Analysis.

Objective: Studies investigating the effects of exergaming in available platforms are still limited. This review aims to systematically identify available studies on physiological intensities of exergaming boxing in able-bodied adults and recategorize them based on different platforms or environments. The meta-analysis further analyzes the physiological responses during exergaming boxing into a set of pooled data for any evidence of outliers, heterogeneity, or publication bias. Materials and Methods: A systematic search was conducted by using databases from Google Scholar, PubMed, and Web of Science. Population, intervention, comparison, and outcomes (PICO) and preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were used in the study selection process for the review. Results: From the 1534 articles examined, 16 articles were included for further analyses. Results indicated that exergaming boxing exhibits a wide range of metabolic equivalent of task (MET) values and intensity, from very light to vigorous, with elements of heterogeneity and bias detected. The Xbox® Kinect boxing platform produced higher MET (mean = 5.3) compared with the Nintendo® Wii™ boxing (mean = 3.8). Conclusion: The results of this review suggest that boxing exergames can produce intensity-adequate physical activity among younger adults that are beneficial for cardiometabolic improvements, regardless of platforms used. Exergaming boxing may be employed as an effective exercise tool to increase energy expenditure and physical activity level in young adults.

Compression and tension behavior of the prosthetic foam materials polyurethane, EVA, Pelite™ and a combination of polyurethane and EVA: a preliminary study.

Materials with low-strength and low-impedance properties, such as elastomers and polymeric foams are major contributors to prosthetic liner design. Polyethylene-Light (Pelite™) is a foam liner that is the most frequently used in prosthetics but it does not cater to all amputees' limb and skin conditions. The study aims to investigate the newly modified Foam Liner, a combination of two different types of foams (EVA + PU + EVA) as the newly modified Foam Liner in terms of compressive and tensile properties in comparison to Pelite™, polyurethane (PU) foam, and ethylene-vinyl acetate (EVA) foam. Universal testing machine (AGS-X, Shimadzu, Kyoto, Japan) has been used to measure the tensile and compressive stress. Pelite™ had the highest compressive stress at 566.63 kPa and tensile stress at 1145 kPa. Foam Liner fell between EVA and Pelite™ with 551.83 kPa at compression and 715.40 kPa at tension. PU foam had the lowest compressive stress at 2.80 kPa and tensile stress at 33.93 kPa. Foam Liner has intermediate compressive elasticity but has high tensile elasticity compared to EVA and Pelite™. Pelite™ remains the highest in compressive and tensile stiffness. Although it is good for amputees with bony prominence, constant pressure might result in skin breakdown or ulcer. Foam Liner would be the best for amputees with soft tissues on the residual limbs to accommodate movement.

Investigation of EMG parameter for transtibial prosthetic user with flexion and extension of the knee and normal walking gait: A preliminary study.

Electromyography signal has been used widely as input for prosthetic's leg movements. C-Leg, for example, is among the prosthetics devices that use electromyography as the main input. The main challenge facing the industrial party is the position of the electromyography sensor as it is fixed inside the socket. The study aims to investigate the best positional parameter of electromyography for transtibial prosthetic users for the device to be effective in multiple movement activities and compare with normal human muscle's activities. DELSYS Trigno wireless electromyography instrument was used in this study to achieve this aim. Ten non-amputee subjects and two transtibial amputees were involved in this study. The surface electromyography signals were recorded from two anterior and posterior below the knee muscles and above the knee muscles, respectively: tibial anterior and gastrocnemius lateral head as well as rectus femoris and biceps femoris during two activities (flexion and extension of knee joint and gait cycle for normal walking). The result during flexion and extension activities for gastrocnemius lateral head and biceps femoris muscles was found to be more useful for the control subjects, while the tibial anterior and also gastrocnemius lateral head are more active for amputee subjects. Also, during normal walking activity for biceps femoris and gastrocnemius lateral head, it was more useful for the control subjects, while for transtibial amputee subject-1, the rectus femoris was the highest signal of the average normal walking activity (0.0001 V) compared to biceps femoris (0.00007 V), as for transtibial amputee subject-2, the biceps femoris was the highest signals of the average normal walking activity (0.0001 V) compared to rectus femoris (0.00004 V). So, it is difficult to rely entirely on the static positioning of the electromyography sensor within the socket as there is a possibility of the sensor to contact with inactive muscle, which will be a gap in the control, leading to a decrease in the functional efficiency of the powered prostheses.