Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies.

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

IMU Sensors Measurements Towards the Development of Novel Prosthetic Arm Control Strategies.

The complexity of the human upper limb makes replicating it in a prosthetic device a significant challenge. With advancements in mechatronic developments involving the addition of a large number of degrees of freedom, novel control strategies are required. To accommodate this need, this study aims at developing an IMU-based control for the HannesARM upper-limb prosthetic device, as a proof-of-concept for new control strategies integrating data-fusion approaches. The natural human control of the upper-limb is based on different inputs that allow adaptive control. To mimic this in prostheses, the implementation of IMUs provides kinematic information of both the stump and the prosthesis to enrich the EMG control. The principle of operation is to decode upper limb movements by using a custom-made system and to replicate them in prosthetic arms improving the control algorithms. To evaluate the system's effectiveness, the custom algorithm's motion extraction was compared to a motion capture system using fifteen able-bodied subjects. The results showed that this system scored 0.16 ± 0.04 and 0.81 ± 0.12 in Root Mean Squared Error and Cross-Correlation compared to the motion capture system. Experimental results demonstrate how this work can extract valuable kinematic information necessary for new and improved control strategies, such as intention detection or pattern recognition, to allow users to perform a broader range of tasks and enhancing in turn their quality of life.

Preliminary Assessment of Two Simultaneous and Proportional Myocontrol Methods for 3-DoFs Prostheses Using Incremental Learning.

Despite progressive developments over the last decades, current upper limb prostheses still lack a suitable control able to fully restore the functionalities of the lost arm. Traditional control approaches for prostheses fail when simultaneously actuating multiple Degrees of Freedom (DoFs), thus limiting their usability in daily-life scenarios. Machine learning, on the one hand, offers a solution to this issue through a promising approach for decoding user intentions but fails when input signals change. Incremental learning, on the other hand, reduces sources of error by quickly updating the model on new data rather than training the control model from scratch. In this study, we present an initial evaluation of a position and a velocity control strategy for simultaneous and proportional control over 3-DoFs based on incremental learning. The proposed controls are tested using a virtual Hannes prosthesis on two healthy participants. The performances are evaluated over eight sessions by performing the Target Achievement Control test and administering SUS and NASA-TLX questionnaires. Overall, this preliminary study demonstrates that both control strategies are promising approaches for prosthetic control, offering the potential to improve the usability of prostheses for individuals with limb loss. Further research extended to a wider population of both healthy subjects and amputees will be essential to thoroughly assess these control paradigms.

A Novel Method for Vibrotactile Proprioceptive Feedback Using Spatial Encoding and Gaussian Interpolation.

OBJECTIVE: The bidirectional communication between the user and the prosthesis is an important requirement when developing prosthetic hands. Proprioceptive feedback is fundamental to perceiving prosthesis movement without the need for constant visual attention. We propose a novel solution to encode wrist rotation using a vibromotor array and Gaussian interpolation of vibration intensity. The approach generates tactile sensation that smoothly rotates around the forearm congruently with prosthetic wrist rotation. The performance of this scheme was systematically assessed for a range of parameter values (number of motors and Gaussian standard deviation). METHODS: Fifteen non-disabled subjects and one individual with congenital limb deficiency used vibrational feedback to control a virtual hand in the target-achievement control test. Performance was assessed by end-point error and efficiency as well as subjective impressions. RESULTS: The results showed a preference for smooth feedback and a higher number of motors (8 and 6 versus 4). With 8 and 6 motors, the standard deviation, determining the sensation spread and continuity, could be modulated through a broad range of values (0.1 - 2) without a significant performance loss. The overall average error and efficiency across these feedback configurations were ∼ 10% and ∼ 30%, respectively. For low values of standard deviation (0.1-0.5), the number of motors could be reduced to 4 without a significant performance decrease. CONCLUSION: The study demonstrated that the developed strategy provided meaningful rotation feedback. Moreover, the results indicate that the Gaussian standard deviation could be used as an independent parameter to encode an additional feedback variable. SIGNIFICANCE: The proposed method is a flexible and effective approach to provide proprioceptive feedback while adjusting the trade-off between sensation quality and the number of vibromotors.

Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach.

Introduction: In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction. Methods: Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees. Results: The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).

Comparative analysis of inverse kinematics methodologies to improve the controllability of rehabilitative robotic devices.

The solution of the inverse kinematics problem in multi-degrees of freedom robots has been tackled, through the last three decades, by several different approaches including analytical, geometrical, differential and numerical methods. All these techniques present their own advantages and drawbacks. However, a guideline on which approach is better to follow, depending on the kind of task to perform and the type of robotic device used, is still missing. In this work, a quantitative comparative analysis of three different inverse kinematics methodologies for the control of rehabilitative robotic devices is proposed, with aim of devising best practices and guidelines for the selection of the most suitable approach. The analyzed methodologies are implemented and numerically tested on two actual devices, specifically an upper-limb exoskeleton and an upper-limb prosthetic arm.

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

BACKGROUND: Cybathlon 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. METHODS: Two 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. RESULTS: The 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. CONCLUSION: Hannes 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.

User-Centered Design and Development of the Modular TWIN Lower Limb Exoskeleton.

For decades, powered exoskeletons have been considered for possible employment in rehabilitation and personal use. Yet, these devices are still far from addressing the needs of users. Here, we introduce TWIN, a novel modular lower limb exoskeleton for personal use of spinal-cord injury (SCI) subjects. This system was designed according to a set of user requirements (lightweight and autonomous portability, quick and autonomous donning and setup, stability when standing/walking, cost effectiveness, long battery life, comfort, safety) which emerged during participatory investigations that organically involved patients, engineers, designers, physiatrists, and physical therapists from two major rehabilitation centers in Italy. As a result of this user-centered process, TWIN's design is based on a variety of small mechatronic modules which are meant to be easily assembled and donned on or off by the user in full autonomy. This paper presents the development of TWIN, an exoskeleton for personal use of SCI users, and the application of user-centered design methods that are typically adopted in medical device industry, for its development. We can state that this approach revealed to be extremely effective and insightful to direct and continuously adapt design goals and activities toward the addressment of user needs, which led to the development of an exoskeleton with modular mechatronics and novel lateral quick release systems. Additionally, this work includes the preliminary assessment of this exoskeleton, which involved healthy volunteers and a complete SCI patient. Tests validated the mechatronics of TWIN and emphasized its high potential in terms of system usability for its intended use. These tests followed procedures defined in existing standards in usability engineering and were part of the formative evaluation of TWIN as a premise to the summative evaluation of its usability as medical device.

Exploring the Embodiment of a Virtual Hand in a Spatially Augmented Respiratory Biofeedback Setting.

Enhancing the embodiment of artificial limbs-the individuals' feeling that a virtual or robotic limb is integrated in their own body scheme-is an impactful strategy for improving prosthetic technology acceptance and human-machine interaction. Most studies so far focused on visuo-tactile strategies to empower the embodiment processes. However, novel approaches could emerge from self-regulation techniques able to change the psychophysiological conditions of an individual. Accordingly, this pilot study investigates the effects of a self-regulated breathing exercise on the processes of body ownership underlying the embodiment of a virtual right hand within a Spatially Augmented Respiratory Biofeedback (SARB) setting. This investigation also aims at evaluating the feasibility of the breathing exercise enabled by a low-cost SARB implementation designed for upcoming remote studies (a need emerged during the COVID-19 pandemic). Twenty-two subjects without impairments, and two transradial prosthesis users for a preparatory test, were asked (in each condition of a within-group design) to maintain a normal (about 14 breaths/min) or slow (about 6 breaths/min) respiratory rate to keep a static virtual right hand "visible" on a screen. Meanwhile, a computer-generated sphere moved from left to right toward the virtual hand during each trial (1 min) of 16. If the participant's breathing rate was within the target (slow or normal) range, a visuo-tactile event was triggered by the sphere passing under the virtual hand (the subjects observed it shaking while they perceived a vibratory feedback generated by a smartphone). Our results-mainly based on questionnaire scores and proprioceptive drift-highlight that the slow breathing condition induced higher embodiment than the normal one. This preliminary study reveals the feasibility and potential of a novel psychophysiological training strategy to enhance the embodiment of artificial limbs. Future studies are needed to further investigate mechanisms, efficacy and generalizability of the SARB techniques in training a bionic limb embodiment.