Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

/pdf/a-review-of-wearable-sensors-and-systems-with-application-in-45qqksiwsq.pdf

A review of wearable sensors and systems with application in rehabilitation.

Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and

/pdf/sensor-networks-for-medical-care-275nw2j123.pdf

Sensor networks for medical care

The Xsens MVN motion capture suit is an easy-to- use, cost efficient system for full-body human motion capture MVN is based on unique, state-of-the-art miniature inertial sensors, biomechanical models and sensor fusion algorithms MVN does not need external cameras, emitters or markers It can thus be used outdoors as well as indoors, there are no restrictions for lighting, it does not suffer from problems of occlusion or missing markers In addition, unique for inertial motion capture technology: the sensor-suit captures any type of movement, including running, jumping, crawling and cartwheels

http://edge.rit.edu/content/P10010/public/PDF/MVN_white_paper.pdf

Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors

Direct measurement of human movement by accelerometry

In this paper, we propose a device for the Position and Orientation (PO showing that the wearable device hereby presented permits the 3-D reconstruction of the movement of the body segment to which it is affixed for time-limited clinical applications.

The development and test of a device for the reconstruction of 3-D position and orientation by means of a kinematic sensor assembly with rate gyroscopes and accelerometers

The analysis of the mechanics of the musculo-skeletal system during the execution of a motor task requires the determination of the instantaneous position and orientation of the body segments involved in relation to an inertial system of reference. By using adequately assembled uniaxial accelerometric sensors, an easy-to-manage measurement system can be obtained that estimates the three-dimensional position and orientation (PO /spl les/10/sup -4/ m: uncertainty of the distance between two ACs; /spl les/10/sup -2/ ms/sup -2/: random error; 0.5/spl middot/10/sup -2/ ms/sup -2/: offset error), the results indicate that none of the two ACs systems analyzed is suitable for body segment P&O estimation in routine biomechanical applications.

Is it feasible to reconstruct body segment 3-D position and orientation using accelerometric data?

Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography.

Measurement of gastrocnemius volume correlated with gait offers a very precise value for step frequency. The system makes an electronic record for storage, interpretation, and transmission. The sys...

Telemonitoring and Telerehabilitation of Patients with Parkinson’s Disease: Health Technology Assessment of a Novel Wearable Step Counter

Fall prevention lacks easy, quantitative and wearable methods for the classification of fall-risk (FR). Efforts must be thus devoted to the choice of an ad hoc classifier both to reduce the size of the sample used to train the classifier and to improve performances. A new methodology that uses a neural network (NN) and a wearable device are hereby proposed for this purpose. The NN uses kinematic parameters assessed by a wearable device with accelerometers and rate gyroscopes during a posturography protocol. The training of the NN was based on the Mahalanobis distance and was carried out on two groups of 30 elderly subjects with varying fall-risk Tinetti scores. The validation was done on two groups of 100 subjects with different fall-risk Tinetti scores and showed that, both in terms of specificity and sensitivity, the NN performed better than other classifiers (naive Bayes, Bayes net, multilayer perceptron, support vector machines, statistical classifiers). In particular, (i) the proposed NN methodology improved the specificity and sensitivity by a mean of 3% when compared to the statistical classifier based on the Mahalanobis distance (SCMD) described in Giansanti (2006 Physiol. Meas. 27 1081?90); (ii) the assessed specificity was 97%, the assessed sensitivity was 98% and the area under receiver operator characteristics was 0.965.

http://iopscience.iop.org/article/10.1088/0967-3334/29/3/N01/pdf

Giovanni Maccioni

Papers

The development and test of a device for the reconstruction of 3-D position and orientation by means of a kinematic sensor assembly with rate gyroscopes and accelerometers

Is it feasible to reconstruct body segment 3-D position and orientation using accelerometric data?

Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography.

Telemonitoring and Telerehabilitation of Patients with Parkinson’s Disease: Health Technology Assessment of a Novel Wearable Step Counter

New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device.