6,270 research outputs found
Comfort-Centered Design of a Lightweight and Backdrivable Knee Exoskeleton
This paper presents design principles for comfort-centered wearable robots
and their application in a lightweight and backdrivable knee exoskeleton. The
mitigation of discomfort is treated as mechanical design and control issues and
three solutions are proposed in this paper: 1) a new wearable structure
optimizes the strap attachment configuration and suit layout to ameliorate
excessive shear forces of conventional wearable structure design; 2) rolling
knee joint and double-hinge mechanisms reduce the misalignment in the sagittal
and frontal plane, without increasing the mechanical complexity and inertia,
respectively; 3) a low impedance mechanical transmission reduces the reflected
inertia and damping of the actuator to human, thus the exoskeleton is
highly-backdrivable. Kinematic simulations demonstrate that misalignment
between the robot joint and knee joint can be reduced by 74% at maximum knee
flexion. In experiments, the exoskeleton in the unpowered mode exhibits 1.03 Nm
root mean square (RMS) low resistive torque. The torque control experiments
demonstrate 0.31 Nm RMS torque tracking error in three human subjects.Comment: 8 pages, 16figures, Journa
Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things
Numerous energy harvesting wireless devices that will serve as building
blocks for the Internet of Things (IoT) are currently under development.
However, there is still only limited understanding of the properties of various
energy sources and their impact on energy harvesting adaptive algorithms.
Hence, we focus on characterizing the kinetic (motion) energy that can be
harvested by a wireless node with an IoT form factor and on developing energy
allocation algorithms for such nodes. In this paper, we describe methods for
estimating harvested energy from acceleration traces. To characterize the
energy availability associated with specific human activities (e.g., relaxing,
walking, cycling), we analyze a motion dataset with over 40 participants. Based
on acceleration measurements that we collected for over 200 hours, we study
energy generation processes associated with day-long human routines. We also
briefly summarize our experiments with moving objects. We develop energy
allocation algorithms that take into account practical IoT node design
considerations, and evaluate the algorithms using the collected measurements.
Our observations provide insights into the design of motion energy harvesters,
IoT nodes, and energy harvesting adaptive algorithms.Comment: 15 pages, 11 figure
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
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