Technology for Successful Aging

With our partners at the University of Virginia we are developing a system of sensors, to monitor the activity of seniors in their residences. We measure motion, footfalls, sleep and restlessness, we have stove sensors and sensing mats, all connected wirelessly to a computer which performs an initial evaluation and data transfer to a secure server for further study. Based upon the monitor data we will implement an intervention to ameliorate functional decline. Focus group studies determine the attitudes, concerns and impressions of the residents and staff. We find that senior's attitude to technology is healthy and they will try helpful approaches. In addition to the statistical comparisons, we model the data using hidden Markov models, integrate or fuse the monitor data with video images, and reason about behavior using fuzzy logic. The results of this work will additionally reduce the workload on caregivers, foster communication between residents and family,and give these seniors independence.The authors are grateful for the support from NSF ITR grant IIS-0428420 and the U.S. Administration on Aging, under grant 90AM3013

Rop: A resource oriented protocol for heterogeneous sensor networks

ABSTRACT: Sensor networks have been an active area of research during the past several years. Much previous work deals with issues related to networks having homogenous sensor nodes. In reality, sensors with different radio coverage, power capacity, and processing capabilities are deployed. In addition, not all of the sensors are mobile or have the same mobility freedom or mobility attributes (e.g. speed). The architecture and routing protocol for this type of heterogeneous sensor network must be based on the resources and characteristics of their member nodes. In this paper, we propose a network model that is adaptively formed according to the resources of its members. A protocol named Resource Oriented Protocol (ROP) was developed to build the network model. This protocol principally entails two phases. In the topology formation phase, nodes report their available resource characteristics, based on which network architecture is optimally built. We stress that due to the existence of nodes with limitless resources, a top-down appointment process can build the architecture with minimum consumption of resources. In the topology update phase, mobile sensors and isolated sensors are accepted into the network with an optimal balance of resources. To avoid overhead of periodic route updates, we use a reactive strategy to maintain route cache. This paper provides encouraging simulation results of ROP in GlomoSim. 1

A physicsbased model for predicting user intent in shared-control pedestrian mobility aids

Abstract — This paper presents a physics-based model approach to infer navigational intent of the user of a walker, based on measuring forces and moments applied to the walker’s handles. Our experiments use two 6-DOF force/moment sensors on the walker’s handles, a 2-D kinematic-dynamic model of the walker and a digital motion capture system to trace the path of the walker. The motion capture system records the path the walker follows while the 6 DOF sensors record the handle forces used to guide the walker along that path. A dynamic model of the walker that determines user navigational intent from force/moment data was developed and validated against the motion capture data streams. This paper describes the development and validation of the model as well as plans for using the model as a path predictor. The inferred user intent will be incorporated into a passive shared steering control system for the walker

A Rule-Based Approach to the Analysis of Elders ’ Activity Data: Detection of Health and Possible Emergency Conditions

In this paper, we present a rule-based approach to the inference of elders ’ activity in two primary application areas: detecting Independent Activities of Daily Living (IADLs) for the detection of anomalies in activity data patterns consistent with arising health issues over a period of time, and the detection of possible emergency conditions passively and unobtrusively. We discuss our efforts using classification techniques leading to the rule-based inference approach, and compare results between the two approaches. The results have shown the viability and validity of knowledge-engineered rules, which outperformed automatically generated rules using random forest supervised learning; the κ correlation coefficient between the classification results of the random forest model and the PDA record was 0.79, with 85 % sensitivity and 93% specificity, compared to κ=0.84, with 91 % sensitivity and 100 % specificity for the knowledge engineered rule aimed at the detection of main meal preparation. The paper also presents experimental field trial results of the rule-based approach demonstrating the utility of the method and future directions for our research