Showing papers by "Paolo Bonato published in 2003"

Wearable sensors/systems and their impact on biomedical engineering

Abstract: ecent advances in miniature devices, as well as mobile and ubiquitous computing, have fostered a dramatic growth of interest for wearable technology. Wearable sensors and systems have evolved to the point that they can be considered ready for clinical application. This is due not only to the tremendous increase in research efforts devoted to this area in the past few years but also to the large number of companies that have recently started investing aggressively in the development of wearable products for clinical applications. Stable trends showing a growth in the use of this technology suggest that soon wearable systems will be part of routine clinical evaluations. The interest for wearable systems originates from the need for monitoring patients over extensive periods of time. This case arises when physicians want to monitor individuals whose chronic condition includes risk of sudden acute events or individuals for whom interventions need to be assessed in the home and outdoor environment. If observations over one or two days are satisfactory, ambulatory systems can be utilized to gather physiological data. An obvious example is the use of ambulatory systems for ECG monitoring, which has been part of the routine evaluation of cardiovascular patients for almost three decades. However, ambulatory systems are not suitable when monitoring has to be accomplished over periods of several weeks or months, as is desirable in a number of clinical applications. Wearable systems are totally nonobtrusive devices that allow physicians to overcome the limitations of ambulatory technology and provide a response to the need for monitoring individuals over weeks or even months. They typically rely on wireless, miniature sensors enclosed in patches or bandages, or in items that can be worn, such as a ring or a shirt. They take advantage of hand-held units to temporarily store physiological data and then periodically upload that data to a database server via a wireless LAN or a cradle that allow Internet connection. The data sets recorded using these systems are then processed to detect events predictive of possible worsening of the patient’s clinical situation or they are explored to assess the impact of clinical interventions. All these aspects of wearable technology are covered by this special issue, which is introduced by four commentaries of physicians who share with the readership their vision on future clinical applications of wearable technology, thus pointing out its tremendous potential. Phil Binkley, M.D., professor of medicine at The Ohio State University Division of Cardiology, shares with us his vision on potential applications of wearable devices in cardiovascular research and clinical practice. Walter Frontera, M.D., Ph.D., chairman of the Department of Physical Medicine and Rehabilitation at Harvard Medical School, provides us with a clinician’s viewpoint of the dramatic improvements in patient management that wearable devices could foster. David G. Standaert, M.D., Ph.D., associate professor of neurology, Harvard Medical School and associate neurologist at Massachusetts General Hospital, points out in his commentary the clinical relevance and potential outcomes of monitoring motor fluctuations in patients with Parkinson’s disease. Finally, Joel Stein, M.D., director of the stroke program and chief medical officer at Spaulding Rehabilitation Hospital, highlights the need for monitoring poststroke hemiplegic patients in the home and outdoor environment in order to assess the impact of clinical interventions and plan more effective rehabilitation strategies. The first five articles of this special issue are focused on the development of sensors and systems. In the first article, Asada et al. describe the evolution of the ring sensor over the past eight years. This is likely the most renowned project in the area of wearable devices. The result of several years of work is a pulse oximetry sensor that allows one to continuously monitor heart rate and oxygen saturation in a totally unobtrusive way. The device is shaped like a ring and thus it can be worn for long periods of time without any discomfort to the subject. The ring sensor is equipped with a low-power transceiver that accomplishes bidirectional communication with a base station, thus allowing one to reconfigure the sensor when necessary and to upload data at any point in time. In a nutshell, this is a “jewel” in the wearable technology arena. The second article, by Park and Jayaraman, demonstrates the great impact on the clinical potential of wearable systems of the Georgia Tech Wearable Motherboard, the result of a revolutionary idea that allowed Dr. Jayaraman’s team to develop a garment (i.e., a shirt) that actually functions as a wearable health monitoring system. This concept has been developed into a product that is now commercially available and allows one to record heart rate, body temperature, motion, position, barrier penetration, and the like in a totally nonencumbering manner. Park and Jayaraman point out a

Muscle fatigue and fatigue-related biomechanical changes during a cyclic lifting task.

Abstract: Study design : Electromyographic and biomechanical methods were utilized to investigate correlations between indexes of localized muscle fatigue and changes in the kinematics and kinetics of motion during a cyclic lifting task. Summary of background data : Recent advances in time-frequency analysis procedures for electromyographicic signal processing provide a new way of studying localized muscle fatigue during dynamic contractions. These methods provide a means to investigate fatigue-related functional impairments in patients with low back pain. Objectives : To study the relationship between localized muscle fatigue and the biomechanics of lifting and lowering a weighted box. Fatigue-related changes in the electromyographicic signal of trunk and limb muscles were evaluated and compared to kinematic and kinetic measures in order to determine whether lifting strategy is modified with fatigue. Methods : A total of 14 healthy male subjects (26 +/- 5 years) cyclically lifted and lowered a 13 kg box (12 lifts/min) for 4.5 minutes. A 5-second static maximum lifting task was included immediately before and after the cyclic lifting task to measure changes in lifting strength and static electromyographicic fatigue indexes. Electromyographic signals from 14 muscle sites (including paravertebral and limb muscles) were measured. Changes in the electromyographicic Instantaneous Median Frequency, a fatigue index, were computed using time-frequency analysis methods. This index was compared with more standardized measures of fatigue, such as those based on electromyographicic median frequency acquired during a static trunk extension test, subjective fatigue measures, and maximal static lifting strength. Biomechanical measures were gathered using a motion analysis system to study kinematic and kinetic changes during the lifting task. Results : During the cyclic lifting task, the electromyographic Instantaneous Median Frequency significantly decreased over time in the paravertebral muscles, but not in the limb muscles. Paravertebral electromyographicic Instantaneous Median Frequency changes were consistent with self-reports of fatigue as well as decreases in trunk extension strength. The magnitude of muscle-specific changes in electromyographicic Instantaneous Median Frequency was not significantly correlated with electromyographicic median frequency changes from the static trunk extension task. The load of the box relative to the maximal static lifting strength significantly affected the electromyographicic Instantaneous Median Frequency changes of paravertebral back muscles. Significant changes with fatigue during the task were found in the angular displacements at the knee, hip, trunk, and elbow. These biomechanical changes were associated with increased peak torque and forces at the L4-L5 vertebral segment. Conclusions : Our results demonstrate correlation between localized muscle fatigue and biomechanical adaptations that occur during a cyclic lifting task. This new technique may provide researchers and clinicians with a means to investigate fatigue-related effects of repetitive work tasks or assessment procedures that might be useful in improving education, lifting ergonomy, and back school programs. Although both the dynamic and static tasks resulted in spectral shifts in the electromyographicic data, the fact that these methods led to different muscle-specific findings indicates that they should not be considered as equivalent assessment procedures.

Neuroadaptive technologies: Applying neuroergonomics to the design of advanced interfaces

Abstract: This article describes an emerging approach to the design of human-machine systems referred to as 'neuroadaptive interface technology' A neuroadaptive interface is an ensemble of computer-based displays and controls whose functional characteristics change in response to meaningful variations in the user's cognitive and/or emotional states Variations in these states are indexed by corresponding central nervous system activity, which control functionally adaptive modifications to the interface The purpose of these modifications is to promote safer and more effective human-machine system performance While fully functional adaptive interfaces of this type do not currently exist, there are promising steps being taken toward their development, and great potential value in doing so--value that corresponds directly to and benefits from a neuroergonomic approach to systems development Specifically, it is argued that the development of these systems will greatly enhance overall human-machine system performance

Data mining of motor patterns recorded with wearable technology

Abstract: Discusses utilizing self-organizing maps to manage large data sets gathered in the field. Dramatic advances in wearable technology suggest that wearable sensors and systems are close to deployment in the clinical field environment. Miniature, wireless sensors are becoming readily available due to the tremendous research efforts focused on this area as well as the great interest demonstrated by the private sector for the associated potential market of these devices. Wearable systems, including base stations and hand-held units, are becoming less cumbersome, with an ever-increasing capacity to receive and store vast amounts of data for extensive periods of time. Further, the growing availability of wireless LANs makes it possible to envision wearable systems from which data can be continuously uploaded, while hand-held units provide a means to communicate with the patient when needed.

Data Mining of Motor Patterns Recorded with Wearable Technology Utilizing Self-Organizing Maps to Manage Large Data Sets Gathered in the Field

Abstract: ecent advances in miniature sensors and wireless technology have made available a new generation of monitoring systems that allow one to record physiological data from individuals carrying on daily activities in the home and outdoor environments. In this article we propose an original approach to the analysis of data gathered using wearable technology, namely to manipulate feature sets derived from physiological signals like a database that one may search through to gain new clinical knowledge using techniques formulated for knowledge discovery in large databases [3]. This is an approach commonly referred to as data mining [2], [5]. We see this novel approach as a fundamental step toward establishing the clinical usefulness of wearable systems. Among the numerous possible applications of wearable systems, we decided to focus our work on ways to assess mobility and systemic responses during the accomplishment of motor activities. This is in keeping with our long-term objective of integrating laboratory and clinical assessment with data gathered in the field in order to design interventions that enhance mobility in individuals with cardio-pulmonary, musculoskeletal, and/or neurological disabilities. Herein we present an example of how data mining can be utilized to explore data sets consisting of angular displacement of body segments and EMG activity recorded over an entire day. The study aims at detecting motor patterns associated with muscular complaints. This is just one of the numerous potential clinical applications of the data mining technique reported here. In the concluding section of this article, we suggest ideas for several applications of this kind. Advances in Wearable Technology The latest innovations in wearable sensors and systems have resulted from significant research efforts to fabricate wireless, unobtrusive devices. Among these innovations is the ring sensor, developed at the MIT d’Arbeloff Laboratory for Information Systems and Technology. The ring sensor is a compact