Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Evaluating Optimality in the Control of Wearable Robotic Devices

Lower-limb wearable robots, such as prostheses and exoskeletons, have the potential to fundamentally transform the mobility of millions of able-bodied and disabled individuals during work, recreation, and/or rehabilitation. Researchers have developed high-performance robotic devices that are lightweight and autonomous, yet we have not seen their widespread adoption as clinical or commercial solutions. One major factor delaying the dissemination of this technology is that our control of these devices remains limited, especially outside controlled laboratory settings. While many adaptive control strategies have been proposed and tested for wearable robotic systems, it is still an open question as to how to provide users with optimal assistance. This is a challenge because a clear definition of 'optimal assistance' has not yet been established, due to the various goals of assistive devices and the complex interactions between robotic assistance and human physiology. Furthermore, it has been difficult to move these systems outside the laboratory environment due to constraints imposed by tethered hardware and sensing equipment, as well as the difficulty associated with quantifying non-steady-state activities. To address this challenge, the goal of the work presented in this dissertation is to further our understanding of how to provide users with optimal assistance from robotic exoskeletons and prostheses outside the laboratory environment. The first theme of this dissertation is the translation of experimental methods (e.g., human-in-the-loop optimization) and measurement tools outside the laboratory environment. The second theme is the evaluation of context-specific objectives for the optimization of wearable robotic devices (e.g., metabolic cost, user preference) in clinical and research settings. This dissertation comprises four primary projects. First, I evaluated the impact that changing the power setting of the BiOM commercial powered ankle prosthesis has on the metabolic cost of transport of individuals with transtibial amputation. This work informs clinical tuning and prescription practices by revealing that, to minimize their metabolic energy consumption, individuals require a higher power setting than the setting chosen by the prosthetist to approximate biological ankle kinetics. Second, I investigated an alternative method for estimating instantaneous metabolic cost using portable wearable sensors. The goal of this project was to accurately estimate instantaneous metabolic cost without relying on indirect calorimetry, and thus enable the use of human-in-the-loop optimization algorithms outside the laboratory environment. I utilized linear regression algorithms to predict instantaneous metabolic cost from physiological signals, and systematically compared a large set of signals to determine which sensor signals contain the highest predictive ability, robust to unknown subjects or tasks. In the third project, I built upon this work and demonstrated that including the derivatives of physiological signals in the linear regression algorithm could improve both the speed and accuracy of instantaneous metabolic cost prediction from portable sensors. Finally, I investigated user preference as an objective for the control of lower-limb robotic exoskeletons. In this work, I demonstrated that exoskeleton users can quickly and precisely identify unique preferences in the characteristics of their ankle exoskeleton assistance in two dimensions simultaneously, and that preference changes with speed, exposure, and prior experience. These results provide insight into how users interact with exoskeletons, and establish important benchmarks for researchers, designers, and future consumers. Together, these four projects evaluate the notion of optimality in the control of wearable robotic systems and lay the foundation for translating optimization protocols outside the laboratory environment.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169808/1/kaingr_1.pd

Towards Safe Autonomy in Assistive Robots

Robots have the potential to support older adults and persons with disabilities on a direct and personal level. For example, a wearable robot may help a person stand up from a chair, or a robotic manipulator may aid a person with meal preparation and housework. Assistive robots can autonomously make decisions about how best to support a person. However, this autonomy is potentially dangerous; robots can cause collisions or falls which may lead to serious injury. Therefore, guaranteeing that assistive robots operate safely is imperative. This dissertation advances safe autonomy in assistive robots by developing a suite of tools for the tasks of perception, monitoring, manipulation and all prevention. Each tool provides a theoretical guarantee of its correct performance, adding a necessary layer of trust and protection when deploying assistive robots. The topic of interaction, or how a human responds to the decisions made by assistive robots, is left for future work. Perception: Assistive robots must accurately perceive the 3D position of a person's body to avoid collisions and build predictive models of how a person moves. This dissertation formulates the problem of 3D pose estimation from multi-view 2D pose estimates as a sum-of-squares optimization problem. Sparsity is leveraged to efficiently solve the problem, which includes explicit constraints on the link lengths connecting any two joints. The method certifies the global optimality of its solutions over 99 percent of the time, and matches or exceeds state-of-the-art accuracy while requiring less computation time and no 3D training data. Monitoring: Assistive robots may mitigate fall risk by monitoring changes to a person’s stability over time and predicting instabilities in real time. This dissertation presents Stability Basins which characterize stability during human motion, with a focus on sit-to-stand. An 11-person experiment was conducted in which subjects were pulled by motor-driven cables as they stood from a chair. Stability Basins correctly predicted instability (stepping or sitting) versus task success with over 90 percent accuracy across three distinct sit-to-stand strategies. Manipulation: Robotic manipulators can support many common activities like feeding, dressing, and cleaning. This dissertation details ARMTD (Autonomous Reachability-based Manipulator Trajectory Design) for receding-horizon planning of collision-free manipulator trajectories. ARMTD composes reachable sets of the manipulator through workspace from low dimensional trajectories of each joint. ARMTD creates strict collision-avoidance constraints from these sets, which are enforced within an online trajectory optimization. The method is demonstrated for real-time planning in simulation and on hardware on a Fetch Mobile Manipulator robot, where it never causes a collision. Fall Prevention: Wearable robots may prevent falls by quickly reacting when a user trips or slips. This dissertation presents TRIP-RTD (Trip Recovery in Prostheses via Reachability-based Trajectory Design), which extends the ARMTD framework to robotic prosthetic legs. TRIP-RTD uses predictions of a person’s response to a trip to plan recovery trajectories of a prosthetic leg. TRIP-RTD creates constraints for an online trajectory optimization which ensure the prosthetic foot is placed correctly across a range of plausible human responses. The approach is demonstrated in simulation using data of non-amputee subjects being tripped.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169822/1/pdholmes_1.pd

Proceedings of the ECCOMAS Thematic Conference on Multibody Dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: ● Formulations and Numerical Methods ● Efficient Methods and Real-Time Applications ● Flexible Multibody Dynamics ● Contact Dynamics and Constraints ● Multiphysics and Coupled Problems ● Control and Optimization ● Software Development and Computer Technology ● Aerospace and Maritime Applications ● Biomechanics ● Railroad Vehicle Dynamics ● Road Vehicle Dynamics ● Robotics ● Benchmark ProblemsPostprint (published version

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

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

Putting artificial intelligence into wearable human-machine interfaces – towards a generic, self-improving controller

The standard approach to creating a machine learning based controller is to provide users with a number of gestures that they need to make; record multiple instances of each gesture using specific sensors; extract the relevant sensor data and pass it through a supervised learning algorithm until the algorithm can successfully identify the gestures; map each gesture to a control signal that performs a desired outcome. This approach is both inflexible and time consuming. The primary contribution of this research was to investigate a new approach to putting artificial intelligence into wearable human-machine interfaces by creating a Generic, Self-Improving Controller. It was shown to learn two user-defined static gestures with an accuracy of 100% in less than 10 samples per gesture; three in less than 20 samples per gesture; and four in less than 35 samples per gesture. Pre-defined dynamic gestures were more difficult to learn. It learnt two with an accuracy of 90% in less than 6,000 samples per gesture; and four with an accuracy of 70% after 50,000 samples per gesture. The research has resulted in a number of additional contributions: • The creation of a source-independent hardware data capture, processing, fusion and storage tool for standardising the capture and storage of historical copies of data captured from multiple different sensors. • An improved Attitude and Heading Reference System (AHRS) algorithm for calculating orientation quaternions that is five orders of magnitude more precise. • The reformulation of the regularised TD learning algorithm; the reformulation of the TD learning algorithm applied the artificial neural network back-propagation algorithm; and the combination of the reformulations into a new, regularised TD learning algorithm applied to the artificial neural network back-propagation algorithm. • The creation of a Generic, Self-Improving Predictor that can use different learning algorithms and a Flexible Artificial Neural Network.Open Acces

Multibody dynamics 2015

This volume contains the full papers accepted for presentation at the ECCOMAS Thematic Conference on Multibody Dynamics 2015 held in the Barcelona School of Industrial Engineering, Universitat Politècnica de Catalunya, on June 29 - July 2, 2015. The ECCOMAS Thematic Conference on Multibody Dynamics is an international meeting held once every two years in a European country. Continuing the very successful series of past conferences that have been organized in Lisbon (2003), Madrid (2005), Milan (2007), Warsaw (2009), Brussels (2011) and Zagreb (2013); this edition will once again serve as a meeting point for the international researchers, scientists and experts from academia, research laboratories and industry working in the area of multibody dynamics. Applications are related to many fields of contemporary engineering, such as vehicle and railway systems, aeronautical and space vehicles, robotic manipulators, mechatronic and autonomous systems, smart structures, biomechanical systems and nanotechnologies. The topics of the conference include, but are not restricted to: Formulations and Numerical Methods, Efficient Methods and Real-Time Applications, Flexible Multibody Dynamics, Contact Dynamics and Constraints, Multiphysics and Coupled Problems, Control and Optimization, Software Development and Computer Technology, Aerospace and Maritime Applications, Biomechanics, Railroad Vehicle Dynamics, Road Vehicle Dynamics, Robotics, Benchmark Problems. The conference is organized by the Department of Mechanical Engineering of the Universitat Politècnica de Catalunya (UPC) in Barcelona. The organizers would like to thank the authors for submitting their contributions, the keynote lecturers for accepting the invitation and for the quality of their talks, the awards and scientific committees for their support to the organization of the conference, and finally the topic organizers for reviewing all extended abstracts and selecting the awards nominees.Postprint (published version

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data