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As upper-limb rehabilitation devices development continue to grow, lower limb rehabilitation devices have limited development. Thus a device that measures weight bearing of heel and forefoot during gait and also produces auditory feedback at certain thresholds were made in this project. The device was constructed based on an ARM-Core microcontroller and was supported by 5 force sensing resistor sensors for each foot. Communications between the modules and the computer as the GUI were established using an UART interface. For testing, sensors were placed beneath the foot using a stretchable cloth so each different subjects’ pressure points were correctly placed. The biofeedback used for this device was by enabling a buzzer and LED embedded to the designed board that turns on when heel strike or push off reached acertain threshold. Testing were divided into phase one and two. From phase one, it was concluded that both push off and heel strike event can have a reading exceeding 8.5% of the body weight. It was then used as a threshold for the biofeedback event. During the biofeedback tests, improvements in the subjects’ walking pattern and reading were noted. Results suggest that biofeedback can change the behavior of the subject. / I takt med att utvecklingen av rehabilitationsapparater i övre extremiteterna fortsätter att växa, har rehabiliteringsanordningar för nedre extremiteterna begränsad utveckling. Således gjordes en anordning som mäter viktbearbetning av häl och framfot under gång och också ger hörselåterkoppling vid vissa trösklar i detta projekt. Enheten var konstruerad baserad på en ARM-Core mikrokontroller och stöds av 5 kraftsavkännande motståndssensorer för varje fot. Kommunikation mellan modulerna och datorn som GUI upprättades medhjälp av ett UART-gränssnitt. För testning placerades sensorer under foten meden töjbar duk så att varje olika försöks tryckpunkter placerades korrekt. Den biofeedback som användes för den här enheten var genom att aktivera en summer och LED inbäddad i det konstruerade kortet som slås på när hälslaget eller skjutstoppet nådde en viss tröskel. Testningen delades in i fas en och två. Frånfas ett drogs slutsatsen att både push off och hälstreffhändelse kan ha en avläsningsom överstiger 8.5% av kroppsvikt. Den användes sedan som en tröskelför biofeedback-händelsen. Under biofeedback-testen noterades förbättringar i försökspersonernas gångmönster och läsning. Resultaten tyder på att biofeedback kan förändra beteendet hos ämnet.

: Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960-0.999, R = 0.922-0.999; RMSE = 0.047-0.242, < 0.001), while GNSSD (small circle, R = 0.999, R = 0.997, RMSE = 0.047 < 0.001; large circle, R = 0.999, R = 0.999, RMSE = 0.027, < 0.001) and the accelerometry derived metric MAG (small circle, R = 0.992, R = 0.983, RMSE = 0.112, < 0.001; large circle, R = 0.997, R = 0.995, RMSE = 0.064, < 0.001) performed best among all models. This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking.

Conventional head-mounted projection displays (HMPDs) contain of a pair of miniature projection lenses, beamsplitters, and miniature displays mounted on the helmet, as well as a retro-reflective screen placed strategically in the environment. We have extened the HMPD technology integrating the screen into a fully mobile embodiment. Some initial efforts of demonstrating this technology has been captured followed by an investigation of the diffraction effects versus image degradation caused by integrating the retro-reflective screen within the HMPD. The key contribution of this research is the conception and development of a mobileHMPD (M-HMPD). We have included an extensive analysis of macro- and microscopic properties that encompass the retro-reflective screen. Furthermore, an evaluation of the overall performance of the optics will be assessed in both object space for the optical designer and visual space for the possible users of this technology. This research effort will also be focused on conceiving a mobile M-HMPD aimed for dual indoor/outdoor applications. The M-HMPD shares the known advantage such as ultralightweight optics (i.e. 8g per eye), unperceptible distortion (i.e. ≤ 2.5%), and lightweight headset (i.e. ≤ 2.5 lbs) compared with eyepiece type head-mounted displays (HMDs) of equal eye relief and field of view. In addition, the M-HMPD also presents an advantage over the preexisting HMPD in that it does not require a retro-reflective screen placed strategically in the environment. This newly developed M-HMPD has the ability to project clear images at three different locations within near- or far-field observation depths without loss of image quality. This particular M-HMPD embodiment was targeted to mixed reality, augmented reality, and wearable display applications.

Half of American women do not sustain recommended levels of physical activity (PA). Wearable activity trackers (WATs) may increase both awareness of, and PA if worn daily. Thus, WATs should include features and designs that encourage daily use. This study aimed to determine WAT features and designs most appealing to women and the motivational effects of various WATs. For this mixed-method pilot study, 15 women each trialed three WATs; documented daily PA levels; and rated their satisfaction with each device's comfort, features, and motivational effect. Additionally, participants shared experiences and feedback in focus groups. Features that promote adoption of WATs among women are, 1) comfort 2) extended battery life, 3) durability, 4) immediate PA feedback, 5) intuitive PA sensing, and 6) programmability. WATs with these preferred qualities may effectively motivate women to increase PA, leading to improved overall health, and reduce healthcare costs.

To aid in the design of future wearable health devices (WHDs), contact pressure between the distal forearm (wrist) and two different wrist-worn devices was investigated in this work. The first device included eight force sensors arranged in series along the length of a wristband to measure the pressure profile. The band also included a tensioner device for manually tightening the band while on a wrist. Testing was done on dummy wrists and the results were statistically significant supporting the hypothesis that areas of the wrist with lower radius of curvature will experience higher contact pressures generally and a faster rate of change in pressure as the band is tightened. The second band included a controller, actuator, and force sensors for actively controlling the contact pressure of a photoplethysmography (PPG) sensor on the wrist during user motion. A total of eight tests were performed on six human subjects to estimate previously unknown design parameters related to contact pressure control of a wrist-worn device. Participants were asked to perform several actions including tapping their finger at different rates, tossing a ball, wrist flexion and extension, and making a fist. The design parameters investigated were system stiffness, range in contact pressure caused by motion, range of motion in the radial direction required to maintain a desired pressure, arterial pulse pressure amplitude and its relation to pressure tolerance, and system response time required to maintain a constant pressure. System stiffness was observed to be greater during motion (dynamic) than during rest (static) and to increase with increasing contact pressure. The change in contact pressure caused by motion was around 18 kPa in some cases and the maximum range of motion to maintain a contact pressure was about 7 mm. The arterial pulse pressure amplitude ranged between 0.05 to 0.3 kPa. It was estimated that a maximum sensor platform speed of 30 mm/s or greater is required to maintain a constant contact pressure during large motion actions such as flexing the wrist up and down. Finally, no significant differences were observed in the PPG signal between states in which the contact pressure was controlled vs. not controlled.

A tool allowing the prediction of the risk of bleeding in patients with hemophilia would be relevant for patients, stakeholders, and policymakers. We performed a systematic review of the literature searching for available risk assessment models to predict the risk of bleeding in people living with hemophilia, and to determine the key risk factors that the ideal model should include. We also systematically review the literature to determine the acceptability and accuracy of wrist-wearable devices to measure physical activity in the general population. Finally, we validated the performance of a risk assessment model for the prediction of the risk for bleeding in people living with hemophilia. We identified the following risk factors for bleeding in people living with hemophilia: plasma factor levels, history of bleeds, physical activity, antithrombotic treatment, and obesity. The FitBit Charge and FitBit Charge HR are the most accurate devices for measuring steps, and the Apple Watch is the most accurate for measuring heart rate. No device proved to be accurate in measuring energy expenditure. The predictive accuracy of the risk assessment model that we validated does not endorse its use to drive decision making on treatment strategies based on the predicted number of bleeds. This might in part be explained by the methods used in the derivation phase. The need for an accurate risk assessment model to predict the risk of bleeding in people living with hemophilia is still unmet. This should be done by including the relevant risk factors identified through our work, with data on physical activity possibly collected using an accurate wrist-wearable device, and through the application of rigorous methods in the derivation and validation phases. / Thesis / Doctor of Philosophy (PhD) / People living with hemophilia lack a coagulation factor and tend to experience spontaneous bleeds, with frequency and intensity that vary between individuals. Predicting who will experience more bleeds would allow for changing the treatment strategies and directing the best resources to the persons that can benefit more. Through this project, we identified the variables that should be considered to estimate the risk for bleeding in people living with hemophilia, namely the blood levels of the lacking coagulation factor, the bleeding history, the physical activity levels, the concomitant treatment with blood thinners, and the presence of obesity. We determined that Fitbit Charge and Charge HR are the most accurate devices for measuring steps and Apple Watch for heart rate. Lastly, we found that an existing tool for predicting the risk of bleeding is not accurate enough to be used in this setting, and a new model should be produced.

No description available.

No / This volume presents state-of-the-art research from a wide area of subjects brought about by the digital convergence of computing, television, telecommunications and the World-Wide Web. It represents a unique snapshot of trends across a wide range of subjects including virtual environments; virtual reality; telepresence; human-computer interface design; interactivity; avatars; and the Internet. Both researchers and practitioners will find it an invaluable source of reference.

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.