This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices have emerged as a promising avenue for providing assistance to individuals with hand disabilities, especially those who may not achieve full recovery through surgical interventions. Nevertheless, prevailing exoskeleton glove systems encounter a multitude of challenges spanning design, control, and human-machine interaction. These challenges have given rise to limitations, such as unwieldy bulkiness, an absence of precise force control algorithms, limited portability, and an imbalance between lightweight construction and the essential functionalities required for everyday activities. To address these challenges, this research undertakes a comprehensive exploration of various dimensions within the exoskeleton glove system domain. This includes the intricate design of the finger linkage mechanism, meticulous kinematic analysis, strategic kinematic synthesis, nuanced dynamic modeling, thorough simulation, and adaptive control. The development of two distinct types of series elastic actuators, coupled with the creation of two diverse exoskeleton glove designs based on differing mechanisms, constitutes a pivotal aspect of this study. For the exoskeleton glove integrated with series elastic actuators, a sophisticated dynamic model is meticulously crafted. This endeavor involves the formulation of a mathematical framework to address backlash and the subsequent mitigation of friction forces. The pursuit of accurate force control culminates in the proposition of a data-driven model-free force predictive control policy, compared with a dynamic model-based force control methodology. Notably, the efficacy of the system is validated through meticulous clinical experiments. Meanwhile, the low-profile exoskeleton glove design with a novel mechanism engages in a further reduction of size and weight. This is achieved through the integration of a rigid coupling hybrid mechanism, yielding pronounced advancements in wearability and comfortability. A deep reinforcement learning approach is adopted for the real-time force planning control policies. A simulation environment is built to train the reinforcement learning agent. In summary, this research endeavors to surmount the constraints imposed by existing exoskeleton glove systems. By virtue of advancing mechanism design, innovating control strategies, enriching perception capabilities, and enhancing wearability, the ultimate goal is to augment the functionality and efficacy of these devices within the realm of assistive applications. / Doctor of Philosophy / This dissertation presents a comprehensive exploration encompassing the design, development, control and the application of reinforcement learning-based force planning for the autonomous grasping capabilities of the innovative assistive robotic exoskeleton gloves. Exoskeleton devices hold significant promise as valuable aids for patients with hand disabilities who may not achieve full recuperation through surgical interventions. However, the present iteration of exoskeleton glove systems encounters notable limitations in terms of design, control mechanisms, and human-machine interaction. Specifically, prevailing systems often suffer from bulkiness, lack of portability, and an inadequate equilibrium between lightweight construction and the essential functionalities imperative for daily tasks. To address these challenges, this research undertakes a comprehensive exploration of diverse facets within the exoskeleton glove system domain. This encompasses a detailed focus on mechanical design, control strategies, and human-machine interaction. To address wearability and comfort, two distinct exoskeleton glove variations are devised, each rooted in different mechanisms. An innovative data-driven model-free force predictive control policy is posited to enable accurate force regulation. Rigorous clinical experiments are conducted to meticulously validate the efficacy of the system. Furthermore, a novel mechanism is seamlessly integrated into the design of a new low-profile exoskeleton glove, thereby augmenting wearability and comfort by minimizing size and weight. A deep reinforcement learning based control agent, which is trained within a simulation environment, is devised to facilitate real-time autonomous force planning. In summary, the overarching objective of this research lies in rectifying the limitations inherent in existing exoskeleton glove systems. By spearheading advancements in mechanical design, control methodologies, perception capabilities, and wearability, the ultimate aim is to substantially enhance the functionality and overall efficacy of these devices within the sphere of assistive applications.

People experiencing mobility deficiencies in their lower limbs caused by genetics, injuries, diseases, etc. struggle with their physical and mental health. The goal of this research is to design an exoskeleton that will connect the upper limb (e.g., arm extension) to the ankle joint during walking movements. We advanced the first prototype of the Workshare Upper Lower Limb (WULL) by only targeting the ankle joint as the lower limb component. We found that this change would have the biggest impact on an individual's walking movements. The benefit of this research will be found in answering the question: will harnessing the kinetic energy from a person's upper limb (e.g., arm extension or arm flexion) to transfer into the ankle joint for gait assistance reduce the lower limb muscle activation during walking movements? A series of experiments were run to test the efficacy of the wearable device. Six participants were fitted to the device and six electromyography (EMG) sensors to track the muscle activation during a comfortable walking pace. This gait analysis study used pressure insoles to calculate ground reaction forces and multiple IMUs to track the individuals' limbs and joints kinetic motion. The overall effectiveness of the device was explored based on the data collected in this study. This device decreased muscle activation of the gastrocnemii medialis and increased the anterior deltoid activation. These results support the goal of the experiment to utilize the upper limbs (anterior deltoid) to assist the lower limbs (ankle joint) during walking.

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A novel exoskeleton robotic system was developed to assist stair climbing. This active demonstrator consists of a motor with a cable system, various sensors, and a control system with a power supply. The objective of this preliminary study is a biomechanical evaluation of the novel system to determine its effectiveness in use. For this purpose, three test persons were biomechan- ically investigated, who performed stair ascents and descents with and without the exoskeleton. Kinematics, kinetics, and muscle activity of the knee extensors were measured. The measured data were biomechanically simulated in order to evaluate the characteristics of joint angles, moments, and reaction forces. The results show that the new exoskeleton assists both the ascent and the descent according to the measured surface electromyography (sEMG) signals, as the knee extensors are relieved by an average of 19.3%. In addition, differences in the interaction between the test persons and the system were found. This could be due to a slightly different operation of the assisting force or to the different influence of the system on the kinematics of the users.

As human dependence on electronic devices grows, there is an emerging need on finding sustainable power sources for low power electronics and sensors. One of the promising possibilities in this space is the human body itself. Harvesting significant power from daily human activities will have a transformative effect on wearables and implantables. One of the main challenges in harvesting mechanical energy from human actions is to ensure that there is no effect on the body itself. For this reason, any intrusive mechanism will not have practical relevance. In this dissertation, novel non-intrusive energy harvesting technologies are investigated that can capture available energy from body, motion, and surroundings. Energy harvesting from the body is explored by developing a wrist-based thermoelectric harvester that can operate at low-temperature gradients. Energy harvesting from motion is investigated by creating a backpack and shoe sole. These devices passively store kinetic energy in a spring that is later released to a generator when it is not intrusive to the user kinematics. Lastly, energy harvesting from immediate surroundings is investigated by designing a two degree of freedom vibration absorber that is excited by electromagnetic fields found in common household appliances. These novel solutions are shown to provide consistent electrical power from wasted energy. Harvester designs are extensively modeled and optimized device architectures are manufactured and tested to quantify the relevant parameters such as output voltage and power density. / Doctor of Philosophy / Energy harvesting is the action to transform energy in the form of heat, relative motion, light, etc. into useful electrical energy. An example of an energy harvester is a solar cell which converts energy in the form of light to electricity. Our body consumes a considerable amount of energy to maintain our body temperature and achieve everyday movements, i.e., walking, jumping, etc. The purpose of this research was to fabricate, model and test wearable energy harvesters in the form of a backpack, a shoe sole, a watch, and a cantilever beam to charge mobile electronics on the go. Electrical energy is harvested from human motion by using the relative displacement between the human torso and a payload. Similarly, the ankle joint is used to produce electricity by using the relative rotation between the foot and shank. The difference in temperature between the ambient air and the human body is used to generate enough electricity to power a wrist watch. Finally, energy is harvested from everyday surroundings by using a cantilever beam which absorbs magnetic fields coming from power cords and able to power sensors.

Joint torque feedback (JTF) is a new and promising means of kinesthetic feedback to provide information to a person or guide them during a motion task. However, little work has been done to apply the torque feedback to a person. This project evaluates the properties of JTF as haptic feedback, starting from the fabrication of a lightweight elbow haptic exoskeleton. A cheap hobby motor and easily accessible hardware are introduced for manufacturing and open-sourced embedded architecture for data logging. The total cost and the weights are $500 and 509g. Also, as the prerequisite step to assess the JTF in guidance, human perceptual ability to detect JTF was quantified at the elbow during all possible static and dynamic joint statuses. JTF slopes per various joint conditions are derived using the Interweaving Staircase Method. For either directional torque feedback, flexional motion requires 1.89-2.27 times larger speed slope, in mNm/(°/s), than the extensional motion. In addition, we find that JTFs during the same directional muscle's isometric contraction yields a larger slope, in mNm/mNm, than the opposing direction (7.36 times and 1.02 times for extension torque and flexion torque). Finally, the guidance performance of the JTF was evaluated in terms of time delay and position error between the directed input and the wearer's arm. When studying how much the human arm travels with JTF, the absolute magnitude of the input shows more significance than the duration of the input (p-values of <0.0001 and 0.001). In the analysis of tracking the pulse input, the highest torque stiffness, 95 mNm/°, is responsible for the smallest position error, 6.102 ± 5.117°, despite the applied torque acting as compulsory stimuli. / Doctor of Philosophy / Joint torque feedback (JTF) is a new and promising means of haptic feedback to provide information to a person or guide them during a motion task. However, little work has been done to apply the torque feedback to a person, such as determining how well humans can detect external torques or how stiff the torque input should be to augment a human motion without interference with the voluntary movement. This project evaluates the properties of JTF as haptic feedback, starting from the fabrication of a lightweight elbow haptic exoskeleton. The novelty of the hardware is that we mask most of the skin receptors so that the joint receptors are primarily what the body will use to detect external sensations. A cheap hobby motor and easily accessible hardware are introduced for manufacturing and open-sourced software architecture for data logging. The total cost and the weight are $500 and 509g. Also, as the prerequisite step to assess the JTF in guidance, human perceptual ability to detect JTF was quantified at the elbow during all possible static and dynamic joint statuses. A psychophysics tool called Interweaving Staircase Method was implemented to derive torque slopes per various joint conditions. For either directional torque feedback, flexional motion requires 1.89-2.27 times larger speed slope, in mNm/(°/s) than the extensional motion. In addition, the muscles' isometric contraction with the aiding direction required a larger slope, in $mathrm{mNm/mNm}$ than the opposing direction (7.36 times and 1.02 times for extension torque and flexion torque). Finally, the guidance performance of the JTF was evaluated in terms of time delay and position error between the directed input and the wearer's arm. When studying how much the human arm travels with JTF, the absolute magnitude of the input shows more significance than the duration of the input (p-values of <0.0001 and 0.001). In the analysis of tracking the pulse input, the highest torque stiffness, 95 mNm/°, is responsible for the smallest position error, 6.102 ± 5.117°, despite the applied torque acting as compulsory stimuli.