Stefano Abbate

A smartphone-based fall detection system

Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers. To reduce the problem of false alarms, the system includes novel techniques for the recognition of those activities of daily living that could be erroneously mis-detected as falls (such as sitting on a sofa or lying on a bed). To limit the intrusiveness of the system, a small external sensing unit can also be used for the acquisition of movement data.

Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey

The problem with accidental falls among elderly people has massive social and economic impacts. Falls in elderly people are the main cause of admission and extended period of stay in a hospital. It is the sixth cause of death for people over the age of 65, the second for people between 65 and 75, and the first for people over 75. Among people affected by Alzheimer’s Disease, the probability of a fall increases by a factor of three. Elderly care can be improved by using sensors that monitor the vital signs and activities of patients, and remotely communicate this information to their doctors and caregivers. For example, sensors installed in homes can alert caregivers when a patient falls. Research teams in universities and industries are developing monitoring technologies for in-home elderly care. They make use of a network of sensors including pressure sensors on chairs, cameras, and RFID tags embedded throughout the home of the elderly people as well as in furniture and clothing, which communicate with tag readers in floor mats, shelves, and walls. A fall can occur not only when a person is standing, but also while sitting on a chair or lying on a bed during sleep. The consequences of a fall can vary from scrapes to fractures and in some cases lead to death. Even if there are no immediate consequences, the long-wait on the floor for help increases the probability of death from the accident. This underlines the importance of real-time monitoring and detection of a fall to enable first-aid by relatives, paramedics or caregivers as soon as possible. Monitoring the activities of daily living (ADL) is often related to the fall problem and requires a non-intrusive technology such as a wireless sensor network. An elderly with risk of fall can be instrumented with (preferably) one wireless sensing device to capture and analyze the 1

Recognition of false alarms in fall detection systems

Falls are a major cause of hospitalization and injury-related deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced.

MIMS: A Minimally Invasive Monitoring Sensor Platform

This paper describes a minimally invasive sensor platform for active and passive monitoring of human movements and physiological signals. Such a system is needed in cases where 24 × 7 monitoring is required, as in older adults with cognitive impairment, dementia and Alzheimers disease. The passive monitoring systems used today are useful only in detecting events after they happen; the accuracy and speed of detection is questionable. The noninvasive nature of such systems does not bring trade off benefits to early detection and prevention of emergency incidents. We compare some existing sensor platforms and present our monitoring approach using minimally invasive wearable sensor device(s). With a Minimally Invasive Monitoring Sensor (MIMS), using advanced intelligent systems, we analyze the physiological signal data preceding potential emergency events in order to predict them quickly. The Virtual Hub is the core component of MIMS, which acts as a gateway between a monitored person and her/his caregivers, as well as a shared access point between active and passive sensing devices. Some preliminary results are presented here from our sleep-related fall study using two heterogeneous sensor systems.

Deploying a Communicating Automatic Weather Station on an Alpine Glacier

Abstract The cost and effort of installing and maintaining an automatic weather station (AWS) on a glacier may be mitigated by the possibility of gathering sensor data in near real-time, and of controlling and programming the station remotely. In this paper we report our experience with upgrading an existing AWS, operating over an Italian glacier, from a mere datalogger into a networked sensing station. Design choices, energy constraints and power-aware programming of the station determined by harsh environment are discussed. Deployment operations and results are described. The upgraded AWS provides low-power connectivity from a remote location and is able to serve as a base station for a wireless sensor network working in the glacier.

Estimation of Energy Consumption for TinyOS 2.x-Based Applications

Abstract The development of energy-efficient applications for wireless sensor networks requires mechanisms and tools for run-time monitoring of energy consumption. We propose a software framework that supports energy profiling of applications for the TinyOS 2.x platform. Measurements are obtained through the insertion of software probes within the code of the operating system. As a consequence, since the APIs are not changed, the programmer is not forced to modify the code of existing applications. The technique has been validated by comparing its results with the values registered by dedicated hardware.

Localization of Shipping Containers in Ports and Terminals Using Wireless Sensor Networks

The most advanced logistics solutions that are currently adopted in ports and terminals use RFID- and GPS-based technologies to identify and localize shipping containers in the yard. Nevertheless, because of the limits of these techniques, the position of containers is still affected by some errors or it cannot be determined in real-time. We propose a non-conventional approach where the position of containers can be continuously determined by means of a wireless sensor network. Each container is equipped with a number of nodes that use wireless communication to detect neighbor containers. At the base station, geometrical constraints and proximity data are combined together to determine the relative positions of containers.

An Integer Linear Programming Approach for Radio-Based Localization of Shipping Containers in the Presence of Incomplete Proximity Information

The most advanced solutions that are currently adopted in ports and terminals use technologies based on radio frequency identification (RFID) and the Global Positioning System (GPS) to identify and localize shipping containers in the yard. Nevertheless, because of the limitations of these solutions, the position of containers is still affected by errors, and it cannot be determined in real time. In this paper, a nonconventional approach is presented: Each container is equipped with nodes that use wireless communication to detect neighbor containers and to send proximity information to a base station. At the base station, geometrical constraints and proximity data are combined to determine the positions of containers. Missing information due to faulty nodes is tolerated by modeling geometrical constraints as an integer linear programming problem. Numerical simulations show that most of the containers can be localized, even when the number of nodes that are affected by faults is on the order of 30%.

Estimation of energy consumption in wireless sensor networks using TinyOS 2.x

Run-time monitoring of energy consumption in wireless sensor networks is a necessary step for the production of energy efficient applications. The demo will show a software system that helps the developer to profile applications based on TinyOS in terms of energy consumption.