Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation

Technological integration of Artificial Intelligence (AI) and machine learning in the Prosthetic and Orthotic industry and in the field of assistive technology has become boon for the Persons with Disabilities. The concept of neural network has been used by the leading manufacturers of rehabilitation aids for simulating various anatomical and biomechanical functions of the lost parts of the human body. The involvement of human interaction with various agents’ i.e. electronic circuitry, software, robotics, etc. has made a revolutionary impact in the rehabilitation field to develop devices like Bionic leg, mind or thought control prosthesis and exoskeletons. Application of Artificial Intelligence and robotics technology has a huge impact in achieving independent mobility and enhances the quality of life in Persons with Disabilities (PwDs)

Performance improvement of a transradial myoelectric prosthesis

ARM2u is a UPC team of both graduate and undergraduate Engineering students that strives to develop a transradial myoelectric prosthesis. The team aims to participate in the powered arm prosthetic race of CYBATHLON, held in Zürich, competing against designs from all over the world. This project compares the prosthesis showcased by the team in December 2021, Andromeda, with the more relevant models of the market, comparing their performance and identifying key improvement areas. This project also reviews the state of Andromeda and the improvements researched and/or implemented since January by ARM2u. It also finds what are the main sources of complaint and rejection among prosthesis users. Solutions for two of these problems, non-intuitive control of the prosthesis and the lack of tactile feedback, are proposed for Andromeda. A new mode to switch mode is designed and implemented, using voice commands transmitted through a phone app. A feedback system that transmits mechanical feedback to the residual limb proportional to the force exerted by the prosthesis is also designed. Both of these improvements are programmed using a new microcontroller intended to be implemented in future iterations of Andromeda. Afterwards, testing of those proposals is conducted, and the impact that their implementation could have is briefly discussed

Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis

Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness. Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks. Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience. Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice

Orthotic and Prosthetic Management in Brachial Plexus Injury: Recent Trends

The brachial plexus is a network of intertwined nerve that controls movement and sensation in arm and hand. Any injury to the brachial plexus can result in partial or complete damage of arm and hand. The surgery is a common indicative procedure in brachial plexus injury in case of non-spontaneous recovery. The loss of function of hand due to injury can be replaced by using body powered or externally powered devices. Recent development in treatment protocol of prosthetic and orthotic science using artificial intelligence helps in rehabilitating the persons with brachial plexus injury to regain his confidence and perform daily activities. Combination of advancement in surgical procedure along with artificially intelligent devices opens a new array to rehabilitate the person with brachial plexus injury

Robotic Rehabilitation System In Malaysia

The goal of this project entitled Robotic Rehabititation System in Malaysia is to examine the purpose of robotics to therapeutic procedures for achieving the finest possible motor and functional recovery for persons with impairments following various diseases such as amputations, life-threatening wounds, brain injury, pain management issues, orthopaedics, pulmonary, spinal cord injuries and strokes. Feasibility study and research concerning robotic rehabilitation system iue prepared for the development of robotic based rehabilitation system in Malaysia to be fulfilled. However, there are significant research challenges in developing and testing rehabilitation robots so that they meet the requirements of the patients. The technology must be capable of improving person's impaired limbs or part of the body. In addition, robots must be able to understand the complexity of human type of movements. Thus, non-robotic rehabilitation centre can be transformed to a robotic based rehabilitation centre by analysing the possibility of transforming the current practice of rehabilitation programs conducted via physiotherapist to an automated rehabilitation activity by means of robot follows with good evidence on how robots might enhance the delivery of robotic rehabilitation to people of all ages

Towards Bidirectional Lower Limb Prostheses: Restoring Proprioception Using EMG Based Vibrotactile Feedback

As a result, they do not effectively replace the lost limb. Electromyography (EMG) control has been widely implemented in upper limb prostheses but is still underdeveloped in lower limb prostheses. The aim of this thesis is to design, develop, and evaluate a novel vibrotactile feedback system in combination with an EMG-controlled powered knee or ankle prosthesis to restore proprioception. This thesis demonstrates that discrete localised vibrations enable proprioceptive sensing for the user through the described sensory feedback system. Three subjects with a major lower limb amputation performed level ground and inclined walking tests under various conditions. The experiments reported in the thesis compare the effects of EMG control with and without sensory feedback on temporal gait symmetry and psychosocial metrics, i.e. cognitive workload assessment, prosthesis embodiment, and confidence. The key results from this thesis are the following: temporal gait symmetry and psychosocial measures tended to improve within and between session, though the results varied widely between subjects. Interference in the rest EMG signal was found when the vibrotactors were activated. Further, subjects were able to distinguish between sensory feedback levels. EMG control initially reduced gait symmetry, but gait symmetry was later increased with sensory feedback. Higher symmetry scores were measured after sensory feedback was turned off, demonstrating learning retention. Similar trends were measured in psychosocial metrics, indicating that the sensory feedback system contributed to perceived improvements of the prosthesis. In summary, results show promising effects of using vibrotactile feedback in combination with EMG control in lower limb prostheses, despite the need to improve system robustness. Longer training with EMG and sensory feedback might improve quality of life of prosthesis users even more

Developing and Optimizing the Artificial Limb Prosthesis Based on pH Change at Neuromuscular Junction

Until now, the biological information that has been made use for the development of artificial limbs based on electro-mechanical coupling is from EMG signals, EEG signal and/or local signals of neuronal excitation. While all these methodologies are successful experimentally, they also possess few drawbacks such as inability to pick up minute neuronal signals, corrosion of the internal electrode leading to toxicity and aberrant reading of those bio-signals. In humans like any vertebrates, the motor movements of the appendages are commanded by the motor area of cerebral cortex voluntarily when a will to act is generated. This is followed by neuronal excitation that passes through NMJ to excite/contract different group of muscles. The muscle excitation is preceded by action potential development that is initiated, maintained and terminated by sequential ionic movements in and out of the muscle cell. The major ions involved are Na and K. The change in these ionic concentrations can lead to change in pH at the NMJ that can be interpreted as information sent by the brain. Thus it was hypothesized that the changes in the pH can accurately mimic the intended changes in the amputated limb muscles, and therefore can be used to turn the user’s desired motion into actual motion of the limb prosthesis. Briefly, the study utilized a pH-to-voltage converter which converts the pH signals of the neuro-muscular junction into an electrical signal (voltage change). A cut-off voltage was assigned above which the limb moves that exactly simulates the role of action potential in muscle contraction. A high fidelity system thus developed can be projected to the movement of fine moving prosthetics like digits