Jack M. Winters

Bio: Jack M. Winters is an academic researcher from Marquette University. The author has contributed to research in topics: Telerehabilitation & Fuzzy logic. The author has an hindex of 27, co-authored 104 publications receiving 4482 citations. Previous affiliations of Jack M. Winters include Arizona State University & The Catholic University of America.

Multiple Muscle Systems: Biomechanics and Movement Organization

Abstract: The study of biomechanics and movement organization has become progressively more complex since the development of advanced computers and improved measurement techniques. This is a critical assessment of the relationship between muscle properties, musculoskeletal dynamics and neuromotor organizational strategies. Researchers in this growing interdisciplinary field have contributed both theoretical and applied material to the volume. The book begins with a treatment of muscle mechanics and modelling approaches for musculoskeletal movement systems, and continues with the principles underlying movement organization.

Analysis of Fundamental Human Movement Patterns Through the Use of In-Depth Antagonistic Muscle Models

Abstract: A nonlinear eighth-order agonist-antagonist muscle model is identified, based on engineering analysis and design criteria, as the desired structure for the broad-range study of a variety of fundamental human joint movements. To complement this structure, systematic protocols, that combine material and geometrical information for each muscle, are developed to obtain the model parameter values needed for the various muscle constitutive equations. The parameters describing the four basic nonlinear relations are easy to visualize, representing the peak curve values and "shape" parameters. Elbow, knee, wrist, and ankle fiexion-extension and eye, wrist, and head rotation are simulated by this same model structure.

Hill-Based Muscle Models: A Systems Engineering Perspective

Abstract: Chapter 1 (Zahalak) provided a brief historical treatment of the early findings that led to the muscle model structure first proposed by A. V. Hill (1938). From a “systems engineering” perspective, this is a phenomenologically based, lumped-parameter model that is based on interpretations of input-output data obtained from controlled experiments. Simply stated, this model consists of a contractile element (CE) that is surrounded, both in series and in parallel, by “passive” connective tissue (Figure 5.1). CE is furthermore characterized by two fundamental relationships: CE tension-length and CE force-velocity. Each of these is modulated by an activation input that is structurally distinct from the location for mechanical coupling between the muscle and the environment (Figure 5.1).

Measurement and modeling of McKibben pneumatic artificial muscles

Abstract: This paper reports mechanical testing the modeling results for the McKibben artificial muscle pneumatic actuator. This device contains an expanding tube surrounded by braided cords. We report static and dynamic length-tension testing results and derive a linearized model of these properties for three different models. The results are briefly compared with human muscle properties to evaluate the suitability of McKibben actuators for human muscle emulation in biologically based robot arms.

Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

Abstract: The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence

A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

Abstract: Background Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring.