Modeling and Design of a Spring-loaded, Cable-driven, Wearable Exoskeleton for the Upper Extremity

An approach to the design of wearable exoskeletons on the basis of simulation of the exoskeleton and a human body model is proposed in this paper. The new approach, addressing the problem of physical human-exoskeleton interactions, models and simulates the mechanics of both the exoskeleton and the human body, which allows designers to effectively analyze and evaluate an exoskeleton design for their function in concert with the human body. A simulation platform is developed by integrating a biomechanical model of the human body and the exoskeleton. With the proposed approach, an exoskeleton is designed for assisting patients with neuromuscular injuries. Results of the analysis and optimization are included

Development of a Wearable Mechatronic Elbow Brace for Postoperative Motion Rehabilitation

This thesis describes the development of a wearable mechatronic brace for upper limb rehabilitation that can be used at any stage of motion training after surgical reconstruction of brachial plexus nerves. The results of the mechanical design and the work completed towards finding the best torque transmission system are presented herein. As part of this mechatronic system, a customized control system was designed, tested and modified. The control strategy was improved by replacing a PID controller with a cascade controller. Although the experiments have shown that the proposed device can be successfully used for muscle training, further assessment of the device, with the help of data from the patients with brachial plexus injury (BPI), is required to improve the control strategy. Unique features of this device include the combination of adjustability and modularity, as well as the passive adjustment required to compensate for the carrying angle

Development and Biomechanical Analysis toward a Mechanically Passive Wearable Shoulder Exoskeleton

Shoulder disability is a prevalent health issue associated with various orthopedic and neurological conditions, like rotator cuff tear and peripheral nerve injury. Many individuals with shoulder disability experience mild to moderate impairment and struggle with elevating the shoulder or holding the arm against gravity. To address this clinical need, I have focused my research on developing wearable passive exoskeletons that provide continuous at-home movement assistance. Through a combination of experiments and computational tools, I aim to optimize the design of these exoskeletons. In pursuit of this goal, I have designed, fabricated, and preliminarily evaluated a wearable, passive, cam-driven shoulder exoskeleton prototype. Notably, the exoskeleton features a modular spring-cam-wheel module, allowing customizable assistive force to compensate for different proportions of the shoulder elevation moment due to gravity. The results of my research demonstrated that this exoskeleton, providing modest one-fourth gravity moment compensation at the shoulder, can effectively reduce muscle activity, including deltoid and rotator cuff muscles. One crucial aspect of passive shoulder exoskeleton design is determining the optimal anti-gravity assistance level. I have addressed this challenge using computational tools and found that an assistance level within the range of 20-30% of the maximum gravity torque at the shoulder joint yields superior performance for specific shoulder functional tasks. When facing a new task dynamic, such as wearing a passive shoulder exoskeleton, the human neuro-musculoskeletal system adapts and modulates limb impedance at the end-limb (i.e., hand) to enhance task stability. I have presented development and validation of a realistic neuromusculoskeletal model of the upper limb that can predict stiffness modulation and motor adaptation in response to newly introduced environments and force fields. Future studies will explore the model\u27s applicability in predicting stiffness modulation for 3D movements in novel environments, such as passive assistive devices\u27 force fields

Application of EMG and Force Signals of Elbow Joint on Robot-assisted Arm Training

Flexion-extension based on the system's robotic arm has the potential to increase the patient's elbow joint movement. The force sensor and electromyography signals can support the biomechanical system to detect electrical signals generated by the muscles of the biological. The purpose of this study is to implement the design of force sensor and EMG signals application on the elbow flexion motion of the upper arm. In this experiments, the movements of flexion at an angle of 45º, 90º and 135º is applied to identify the relationship between the amplitude of the EMG and force signals on every angle. The contribution of this research is for supporting the development of the Robot-Assisted Arm Training. The correlation between the force signal and the EMG signal from the subject studied in the elbow joint motion tests. The application of sensors tested by an experimental on healthy subjects to simulating arm movement. The experimental results show the relationship between the amplitude of the EMG and force signals on flexion angle of the joint mechanism for monitoring the angular displacement of the robotic arm. Further developments in the design of force sensor and EMG signals are potentially for open the way for the next researches based on the physiological condition of each patient

iHandU Simulator - Towards a medical education tool for wrist rigidity assessment

During Deep Brain Stimulation (DBS) surgery, electrodes are implanted in the patient's brain in order to alleviate motor symptoms of common disorders such as Essential Tremor (ET) and Parkinson's Disease (PD). Stimulation parameters and electrode position are adjusted during surgery, chosen in order to obtain the best improvement in the patient's symptoms. The most commonly assessed symptom is muscular rigidity, which is characterized by an increased resistance to movement marked by a permanently elevated muscle contraction in response to a passive stretch. The assessment typically results from a series of flexions and extensions of a chosen joint, and consists of a qualitative improvement given according to a subjective scale, which is susceptible to errors, and has a verified lack of consistency among different clinicians. Robotic simulation of this behavior is of interest not only as a way to develop an educational tool for a unified method of rigidity assessment, but also in the context of aiding the development of decision support systems such as the iHandU, a wearable device in development at INESC-TEC

Development of a Dynamical Systems Model and Adaptive Intervention Strategy for Stroke Rehabilitation

Each year, approximately 795000 people experience stroke in the United States. After stroke onset, about 80% of patients suffer from hemiparesis, the weakness of face or limb on one side. These people outside clinical setting may develop learned nonuse, which may result in long-term limitation in the outcome of motor recovery. Interventions such as the Constraint Induced Movement Therapy has shown promise in reversing nonuse. However, many chronic individuals do not have access to such training programs. Therefore, some novel tools capable of continuous monitoring patients\u27 health status and furthermore providing appropriate interventions for patients in ambient setting is required to optimize stroke rehabilitation.Dynamical systems modeling combined with wearable technologies may allow to quantitatively describe nonuse evolution. We developed and validated a pendulum-based dynamical model using experimental and simulated motion data. Without direct access to internal torques, we proposed an inverse dynamics-based metric to quantify and compare motor performance between limbs. The primary outcome measure is RMSE between the simulated driving torque for experimental and reference motions. Using RMSEs, we defined a novel within-person comparison factor w participant limb [w], and compared it to the Fugl-Mayer Assessment score. Our dynamic model is capable of mimicking upper-extremity shoulder flexion dynamics. RMSE is sensitive to differences in motor performance between limbs for both groups. Finally, the factor w participant limb [w] is related to post-stroke severity. The arm dynamical model may have great potential for monitoring time-varying motor impairment using noninvasive sensing.Markov decision process (MDP) is a comparatively simple approach of simulation modelling. We implemented MDP to understand the primary factors behind human dynamic decision making on limb choice during rehabilitation. The model showed good performance in understanding the crucial motivators (or barriers) underlying patients\u27 behaviors. We found that a patient with higher motivation, greater perceived benefits of paretic-limb use, and milder motor impairment, would show a better adherence to using paretic limb in physical activity, which suggests that we may provide related interventions in clinical practice to promote a better recovery outcome. MDP modelling may be suggestive in designing cost-effective adaptive intervention for stroke rehabilitation