Arm Prosthesis Research Articles

Investigation and experimental evaluation of a mono-pixel X-ray system in healthcare

Abstract The acquisition of two-dimensional X-ray images (of non-human objects) is quite expensive, primarily due to the costly flat-panel or line detectors. Reducing these costs could open up new possibilities in healthcare applications, such as inspecting limb prostheses for cracks and defects or safety-related material testing of helmets and child seats. In this contribution, two different setups of low-cost mono-pixel X-ray scanners are described: a small scanner with a working volume of 20 x 20 x 20 cm3 and a large system with a volume of 150 x 230 x 110 cm3. Using these novel mono-pixel X-ray scanning systems, various objects - ranging from an X-ray phantom and a human skull phantom to a high-end limb prosthesis with integrated electronics - were scanned and investigated. Compared to conventional X-ray systems, the mono-pixel systems can depict these objects without the distortion problems of cone beam detectors. Although the scanning procedure is time-consuming, the results are quite promising. Mono-pixel X-ray-scanning is a novel modality capable of scanning largescale objects with low-dose radiation. While it is currently not suitable for human applications due to the long scanning times, it can still be used for the non-destructive testing of large-scale medical devices, such as e.g. arm and leg prosthesis.

Bioinspired Head-to-Shoulder Reference Frame Transformation for Movement-Based Arm Prosthesis Control

Bioinspired Head-to-Shoulder Reference Frame Transformation for Movement-Based Arm Prosthesis Control

Investigation of adaptive muscle synergy modulated motor responses to grasping perturbations

This study investigated how muscle synergies adapt in response to unexpected changes in object weight during lifting tasks. The aim was to discover which motor control strategies individuals use to maintain their grasping performance. Muscle synergies were extracted from the muscle activity of fifteen healthy participants who lifted objects of identical appearance but varying weights in a randomized order, which introduced artificial perturbations. Reaching and manipulation phases of object lifting were analyzed using constrained non-negative matrix factorization and k-means clustering. Participants exhibited a perturbation-independent and thus consistent recruitment of spatial synergy components, while significant adaptations in muscle synergy activation occurred in response to unexpected perturbations. Perturbations caused by unexpectedly heavy objects led to delayed and gradual increases in muscle synergy activation until the force required to lift the object was reached. In contrast, perturbations caused by lighter objects led to reductions in excess muscle synergy activation occurring later. Sensorimotor control maintains the modularity of muscle synergies. Even when external mechanical perturbations occur, the grasping performance is preserved, and control is adapted solely through muscle synergy activation. These results suggest that using pure spatial synergy components as control signals for myoelectric arm prostheses may prevent them from malfunctioning due to external perturbations.

Smart ArM: a customizable and versatile robotic arm prosthesis platform for Cybathlon and research

BackgroundIn the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants.MethodsTo meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics.ResultsThe Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity.ConclusionThese application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.

Optimizing Sensor Placement and Machine Learning Techniques for Accurate Hand Gesture Classification

Millions of individuals are living with upper extremity amputations, making them potential beneficiaries of hand and arm prostheses. While myoelectric prostheses have evolved to meet amputees’ needs, challenges remain related to their control. This research leverages surface electromyography sensors and machine learning techniques to classify five fundamental hand gestures. By utilizing features extracted from electromyography data, we employed a nonlinear, multiple-kernel learning-based support vector machine classifier for gesture recognition. Our dataset encompassed eight young nondisabled participants. Additionally, our study conducted a comparative analysis of five distinct sensor placement configurations. These configurations capture electromyography data associated with index finger and thumb movements, as well as index finger and ring finger movements. We also compared four different classifiers to determine the most capable one to classify hand gestures. The dual-sensor setup strategically placed to capture thumb and index finger movements was the most effective—this dual-sensor setup achieved 90% accuracy for classifying all five gestures using the support vector machine classifier. Furthermore, the application of multiple-kernel learning within the support vector machine classifier showcases its efficacy, achieving the highest classification accuracy amongst all classifiers. This study showcased the potential of surface electromyography sensors and machine learning in enhancing the control and functionality of myoelectric prostheses for individuals with upper extremity amputations.

Simple recognition of hand gestures using single-channel EMG signals.

Electromyography (EMG) signals are used for many different purposes, such as recording and measuring the electrical activity generated by varying the body's skeletal muscles. Biosignals are different types of biomedical signals, like EMG signals, which can be used for the neural linkage with computers and are obtained from a particular part of the body such as tissue, muscle, organ, or cell system like the nervous system. Surface electromyography (SEMG) is a non-invasive method that can be used as an effective system for controlling upper arm prostheses. This study focused on classifying the five types of distinct finger movements investigated in four unique channels.We have used a classification technique, the k-nearest neighbors (KNN), to categorize the collected samples. Two time-domain features, (a) maximum (Max) and (b) minimum (Min), were used with one of these three features separately: mean absolute value (MAV), root mean square (RMS), and simple square integral (SSI) to classify gestures. We chose classification accuracy as a criterion for evaluating the effectiveness of every classification. We figured out that the first grouping, that is, (MAV, Max, Min), was the best choice for classification. The accuracy percentage in the four channels for the first group was 91.0%, 89.9%, 89.8%, and 96.0%, respectively.

Patient-reported function, quality of life and prosthesis wear in adults born with one hand: a national cohort study.

We invited individuals aged above 16 years with a congenital transverse reduction deficiency at and above the wrist born in Norway between 1970 and 2006 to complete the short version of the Disabilities of the Arm, Shoulder and Hand Outcome Measure, the 5-Level EuroQoL-5-Dimension instrument, the RAND 36-Item Short Form Health Survey and a single-item questionnaire on arm function, appearance, pain and prosthesis wear. Of 154 eligible participants, 58 (38%) responded. Their scores were not different from the general population. All had been offered prostheses, and 56 (97%) had been fitted at a median age of 1 year (interquartile range 0-2.8). Of the participants, 37 (64%) were still prosthesis wearers, while 21 (36%) were non-wearers or using gripping devices only. Prosthesis wearers had higher levels of 'vitality' as assessed by the RAND-36 and rated their arm appearance higher, but there were no other score differences, indicating that prosthesis rejection is not associated with worse functional outcomes.Level of evidence: III.

Development of Customized Passive Arm Prosthetics by Integrating 3D Printing and Scanning Technology

Severe hand injuries resulting from accidents can lead to the traumatic loss of an upper limb. The provision of an assistive device, such as an arm prosthesis, can help patients regain their courage and motivation to engage with the public. This project aimed to integrate 3D scanning and printing technology to develop a customized passive arm prosthesis for individuals who have experienced shoulder disarticulation due to a traumatic accident. The fabrication of the arm prosthesis was specifically designed and tailored to meet each patient’s unique needs and preferences. The prosthesis design was created based on 3D scan files obtained from the patients. Finite element analysis was employed to analyze the design and determine the optimal materials to use, as well as to optimize the overall design. The finalized model was then converted into STL file format and G-code for the 3D printing process. To ensure ease of use, the entire arm prosthesis and a harness system were assembled, and multiple tests and fittings were conducted on the patient. Several prototypes of the arm prosthesis were fabricated to achieve the best fit for each individual patient.

Identifying a Upper-Limb Phase-Dependent Variable under Perturbations for Powered Prosthesis Arm Control.

This paper investigates upper-limb kinematic reaching responses during a mechanical perturbation to understand interjoint arm coordination used towards powered prosthesis control development. Common prosthesis arm controllers use electromyography sensors with data-driven models to decode muscle activation signals in controlling prosthesis joint movements. However, these control approaches produce non-natural, discrete movements with no guarantee the controller can react to unexpected disturbances during continuous task motion. Determining a continuous phase-dependent variable for measuring a human's progression during reaching can derive a time-invariant kinematic function to control the prosthesis joint in a natural, continuous manner. A perturbation experimental study was conducted across three participants in evaluating the shoulder and elbow joint kinematics to examine the existence of a phase shift during reaching. Experimental results demonstrated the effects of arm proximal-distal interjoint coordination that validated the proposed mechanical phase variable of the shoulder used in parameterizing elbow joint kinematic for reaching. This could allow for a continuous phase-based control strategy that can handle disturbances to achieve arm reaching in prosthesis control.

Walking ability of individuals fitted with transfemoral bone-anchored prostheses: A comparative study of gait parameters.

This study presents the walking abilities of participants fitted with transfemoral bone-anchored prostheses using a total of 14 gait parameters. Two-centre retrospective cross-sectional comparative study. Research facilities equipped with tridimensional motion capture systems. Two control arms included eight able-bodied participants arm (54 ± 9 years, 1.75 ± 0.07 m, 76 ± 7 kg) and nine participants fitted with transfemoral socket-suspended prostheses arm (59 ± 9 years, 1.73 ± 0.07 m, 80 ± 16 kg). The intervention arm included nine participants fitted with transfemoral bone-anchored prostheses arm (51 ± 13 years, 1.78 ± 0.09 m, 87.3 ± 16.1 kg). Fitting of transfemoral bone-anchored prostheses. Comparisons were performed for two spatio-temporal, three spatial and nine temporal gait parameters. The cadence and speed of walking were 107 ± 6 steps/min and 1.23 ± 0.19 m/s for the able-bodied participants arm, 88 ± 7 steps/min and 0.87 ± 0.17 m/s for the socket-suspended prosthesis arm, and 96 ± 6 steps/min and 1.03 ± 0.17 m/s for bone-anchored prosthesis arm, respectively. Able-bodied participants and bone-anchored prosthesis arms were comparable in age, height, and body mass index as well as cadence and speed of walking, but the able-bodied participant arm showed a swing phase 31% shorter. Bone-anchored and socket-suspended prostheses arms were comparable for age, height, mass, and body mass index as well as cadence and speed of walking, but the bone-anchored prosthesis arm showed a step width and duration of double support in seconds 65% and 41% shorter, respectively. Bone-anchored and socket-suspended prostheses restored equally well the gait parameters at a self-selected speed. This benchmark data provides new insights into the walking ability of individuals using transfemoral bionics bone-anchored prostheses.

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

One of the biggest problems faced by amputees is obtaining a suitable low-cost prosthesis. To address this problem, the design and implementation of a transradial prosthesis controlled by electroencephalographic (EEG) signals was carried out. This prosthesis is an alternative to prostheses using electromyographic (EMG) signals, which are very complex and exhausting for the patient to execute.We collected EEG signal data using the Emotiv Insight Headset, which were then processed to control the movement of the prosthesis, known as the Zero Arm. Additionally, we incorporated Machine Learning algorithms to classify different types of objects and shapes. The prosthesis also features a haptic feedback system, which simulates the function of mechanoreceptors in the skin, providing the user with a sense of touch when using the prosthesis.Our research has yielded a viable and cost-effective prosthetic limb. We utilized 3D printing and easily obtainable servomotors and controllers, making the prosthesis affordable and accessible.Performance tests of the Zero Arm prosthesis have yielded promising results. The prosthesis demonstrated an average success rate of 86.67% across various tasks, indicating its reliability and effectiveness. Additionally, the prosthesis has an average recognition rate of 70% for different types of objects, a noteworthy accomplishment.

A low-cost AR application to control arm prosthesis

This paper presents an augmented reality application to assist with myoelectric prostheses control for people with limb amputations. For that, we use the low-cost Myo armband coupled with low-level signal processing methods specifically built to control filters’ levels and processing chain. In particular, we use deep learning techniques to process the signals and to accurately identify seven different hand gestures. From that, we have built an augmented reality projection of a hand based on AprilTag markers that displays the gesture identified by the deep learning techniques. With the aim to properly train the gesture recognition system, we have built our own dataset with nine subjects. This dataset was combined with one publicly available to work with the data of 24 subjects in total. Finally, three different deep learning architectures have been comparatively studied, achieving high accuracy values (being 95.56% the best one). This validates our hypothesis that it is possible to have an adaptive platform able to fast learn personalized hand/arm gestures while projecting a virtual hand in real-time. This can reduce the adaptation time to myoelectric prostheses and improve the acceptance levels.

Adaptive Control for Joint Module of Bionic Arms

Minimizing the influence of unknown external disturbances while satisfying complex control requirements in daily life is one of the difficulties of bionic arm control. However, the control of modular joints often ignores the dynamic characteristics and coupling relationship of the system, which can not meet the control requirements of a bionic arm prosthesis. In addition, the large vibration caused by unknown interference is not conducive to human-computer interaction. In order to solve this problem, we proposed an adaptive feedback control (AFC) method based on magnetic field-oriented control for the joint module to realize intelligent control of the bionic arm. The disturbance of the joint module is obtained by real-time parameter calculation. At the same time, the controller regulated the output voltage to eliminate the influence of uncertain disturbances. Theoretical analysis and simulation results show that the controller has a good effect on improving the dynamic performance of the joint module system, and maintaining the stability of the system in the interference, satisfying the requirements of bionic arm control.

Implementation of a neural network of low computational cost for its application in arm prostheses

A prostheses implementation represents a design challenge in its different stages. The control systems and the total system cost play a very important role. In this work, a control proposal is presented using artificial neural networks (ANN) for pattern recognition using electromyographic (EMG) signals, which are obtained from the arm muscle (biceps). A single channel EMG surface sensor is used to acquire the EMG signals and by means of adjacent windows the feature extraction is carried out in order to reduce the input values to the neural network. The neural network is trained with the features extracted from the EMG signals, using a method of muscle tension thresholds for activation and a labeling technique for the output called One Hot Encode. The resulting ANN was embedded in a low-cost microcontroller and an accuracy of approximately 93% was achieved.

Design and Implementation of an Anthropomorphic Robotic Arm Prosthesis

The development and manufacture of prosthetic limbs is one of the important tendencies of the development of medical techniques. Taking into account the development of modern electronic technology and automated systems and its mobility and compactness, the actual task is to create a prosthesis that will be close to a fully functioning human limb in its anthropomorphic properties and will be capable of reproducing its basic actions with a high accuracy. The paper analyzes the main directions in the development of a control system for electronic limb prostheses. The description and results of the practical implementation of a prototype of an anthropomorphic prosthetic arm and its control system are presented in the paper. We developed an anthropomorphic multi-finger artificial hand for utilization in robotic research and teaching applications. The designed robotic hand is a low-cost alternative to other known 3D printed robotic hands and has 21 degrees of freedom—4 degrees of freedom for each finger, 3 degrees for the thumb and 2 degrees responsible for the position of the robotic hand in space. The open-source mechanical design of the presented robotic arm has mass-dimensional and motor parameters close to the human hand, with the possibility of autonomous battery operation, the ability to connect different control systems, such as from a computer, an electroencephalograph, a touch glove.

Patient-reported Outcomes for Children With Unilateral Congenital Below Elbow Deficiency.

The Patient Reported Outcomes Measurement Information System (PROMIS ® ) is a validated tool used to evaluate different domains of function in patients with chronic health conditions. This tool has not been validated in children with unilateral congenital below elbow deficiency (UCBED). The purpose of this study was to determine whether PROMIS discerns functional impairment for children with UCBED and whether children with UCBED differ from the general population with respect to PROMIS outcomes. We hypothesized that children with UCBED report mild impairment in upper extremity function but normal mobility, pain interference and peer relations. A retrospective chart review of children aged 5 to 17 years with a diagnosis of UCBED who completed a PROMIS questionnaire at their clinic visit at the [blinded locations] was conducted between April 1, 2017 and March 31, 2020. The mean PROMIS scores of UCBED patients were compared with that of the general reference population. Mann Whitney and ANOVA tests were used to explore the differences across the PROMIS upper extremity function domain by arm length and prosthesis use. Fifty-five children (28 boys) with a mean age of 11±3.6 years met the inclusion criteria. Children with UCBED had similar PROMIS scores as the reference population in mobility (51.9±6.2), peer relations (53.5±9.4), and pain interference (40.1±7.2), with mild impairment in the upper extremity function (44.3±10.7). Compared with the 8 to 17-year-old cohort, the parent-proxy (5 to 7-year-old group) reported significantly more upper extremity function impairment (31.3±5.9) vs (48.0±8.8) ( P =0.000). The two age groups did not differ with respect to mobility, pain interference and peer relations. Our study confirms previous findings that children with UCBED report upper extremity function, peer relationships, pain interference, and mobility, similar to the reference population. In addition, parents of younger children with UCBED report more upper extremity functional impairment than is self-reported by older children with UCBED. Prognostic Level III (comparison with reference population).

Toward improving control performance of myoelectric arm prosthesis by adding wrist position feedback.

Wearing a myoelectric prosthesis is a basic way for limb amputees to restore their lost limb functions in the activities of daily living (ADLs). However, it is estimated that around 40% of amputees refuse the prosthesis. One of the primary reasons would be that the current prostheses lack appropriate sensory feedback. Currently, the amputees only depend on their visual feedback (Vis-FB) when using their arm prostheses. It would be difficult for them to accurately control the wrist position, which is vital for flexible manipulation in ADLs. This manuscript designed a myoelectric arm prosthesis with wrist position feedback (WP-FB). To study the effect level of position feedback on prosthetic control, two tests were performed. The vibrotactile perception range test aims to analyze the perception sensitivity of the vibration in humans and obtain the optimal perception range utilized in the sensory feedback test. The sensory feedback test analyzes the effectiveness of the position feedback by comparing three feedback methods of Vis-FB, WP-FB, and a combination of Vis-FB and WP-FB (VP-FB). These tests were conducted by asking six able-bodied subjects to perform 20 movement combinations of five target positions. The WP-FB was transiently activated with five vibrating motors embedded in an armband to stimulate the arm stump when the prosthetic wrist rotates to the target positions. Our experimental results showed that when WP-FB was added to the prosthetic control, the absolute angular error (AAE) of the prosthetic wrist declined from 4.50° to 1.08° while the success rate 3 (SR3) increased from 0.34 to 0.84, respectively. This study demonstrates the importance of WP-FB to the effective control of the arm prosthesis.

Improved Classification Accuracy of Hand Movements Using Softmax Classifier and Kalman Filter.

Accurate identification of the intended hand movement from the surface Electromyography (sEMG) data is desired for effective control of myoelectric lower arm prostheses. This study improves the classification accuracy of hand gestures by using feature arrays, Kalman filter (KF), and a Softmax classifier. We use data from BioPatRec database to classify ten hand movements performed by 17 participants. The proposed classifier achieved 95.3% accuracy without KF, and 99.3% accuracy when KF was used to smooth the training data.

Benefits of the Cybathlon 2020 experience for a prosthetic hand user: a case study on the Hannes system

BackgroundCybathlon championship aims at promoting the development of prosthetic and assistive devices capable to meet users’ needs. This paper describes and analyses possible exploitation outcomes of our team’s (REHAB TECH) experience into the Powered Arm Prosthesis Race of the Cybathlon 2020 Global Edition, with the novel prosthetic system Hannes. In detail, we present our analysis on a concurrent evaluation conducted to verify if the Cybathlon training and competition positively influenced pilot’s performance and human-technology integration with Hannes, with respect to a non-runner Hannes user.MethodsTwo transradial amputees were recruited as pilots (Pilot 1 and Pilot 2) for the Cybathlon competition and were given the polyarticulated myoelectric prosthetic hand Hannes. Due to COVID-19 emergency, only Pilot 1 was trained for the race. However, both pilots kept Hannes for Home Use for seven weeks. Before this period, they both participated to the evaluation of functionality, embodiment, and user experience (UX) related to Hannes, which they repeated at the end of the Home Use and right after the competition. We analysed Pilot 1’s training and race outcomes, as well as changes in the concurrent evaluation, and compared these results with Pilot 2’s ones.ResultsThe Cybathlon training gradually improved Pilot 1’s performances, leading to the sixth place with a single error in task 5. In the parallel evaluation, both pilots had an overall improvement over time, whereas Pilot 2 experienced a deterioration of embodiment. In detail, Pilot 1, who followed the training and raced the Cybathlon, improved in greater way.ConclusionHannes demonstrated to be a valuable competitor and to perform grasps with human-like behaviors. The higher improvements of Pilot 1, who actively participated in the Cybathlon, in terms of functionality, embodiment and UX, may depend on his training and engagement in the effort of achieving a successful user-prosthesis interaction during the competition. Tasks based on Cybathlon’s ones could improve the training phase of a prosthetic user, stimulating dexterity, prosthetic integration, and user perception towards the prosthesis. Likewise, timed races or competitions could facilitate and accelerate the learning phase, improving the efficiency and efficacy of the process.

Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user

BackgroundAssistive technologies, such as arm prostheses, are intended to improve the quality of life of individuals with physical disabilities. However, certain training and learning is usually required from the user to make these technologies more effective. Moreover, some people can be encouraged to train more through competitive motivation.MethodsIn this study, we investigated if the training for and participation in a competitive event (Cybathlon 2020) could promote behavioral changes in an individual with upper limb amputation (the pilot). We defined behavioral changes as the active time while his prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot’s ability to finely modulate his movement speeds. The investigation was based on extensive home-use data from the period before, during and after the Cybathlon 2020 competition.ResultsRelevant behavioral changes were found from both quantitative and qualitative analyses. The pilot’s home use of his prosthesis nearly doubled in the period before the Cybathlon, and remained 66% higher than baseline after the competition. Moreover, he improved his speed modulation when controlling his prosthesis, and he learned and routinely operated new movements in the prosthesis (wrist rotation) at home. Additionally, as confirmed by semi-structured interviews, his self-perception of the prosthetic arm and its functionality also improved.ConclusionsAn event like the Cybathlon may indeed promote behavioral changes in how competitive individuals with amputation use their prostheses. Provided that the prosthesis is suitable in terms of form and function for both competition and at-home daily use, daily activities can become opportunities for training, which in turn can improve prosthesis function and create further opportunities for daily use. Moreover, these changes appeared to remain even well after the event, albeit relevant only for individuals who continue using the technology employed in the competition.