Alessio Vecchio

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.

Smartphone-based crowdsourcing for network monitoring: Opportunities, challenges, and a case study

Smartphone-based crowdsourcing fosters the rise of radically novel systems and applications in the context of network monitoring. This article discusses the most significant opportunities offered by this approach, and the major challenges that have to be faced. Our experience in building a smartphone-based crowdsourcing system, Portolan, is also included to provide a practical background to the discussion and to demonstrate the possible benefits.

Increasing the efficiency of preamble sampling protocols for wireless sensor networks

Applications designed for event driven monitoring represent a challenging class of applications for wireless sensor networks. They are a special kind of monitoring applications, since they usually need low data rates, but also require mechanisms for low latency and asynchronous communication. In this paper we will focus on optimizations at the MAC layer that enable low energy consumption when contention-based protocols are adopted. We present B-MAC+, an enhanced version of a widely adopted MAC protocol, and we show that substantial improvements, in terms of network lifetime, can be reached over the original protocol.

An On-Node Processing Approach for Anomaly Detection in Gait

A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can only be applied to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we propose is designed to achieve continuous monitoring of gait in a completely unsupervised fashion, requiring the use of a single waist-mounted accelerometer. The users gait patterns are automatically learned using specific acceleration-based features, while anomaly detection is used to capture subtle changes in the way the user walks. All the required processing can be executed in real time on the wearable device. The method was evaluated with 30 volunteers, who simulated a knee flexion impairment. On average, our method obtained ~84% accuracy in the recognition of abnormal gait segments lasting ~5 s. Prompt detection of gait anomalies could enable early intervention and prevent falls.

Non-intrusive Patient Monitoring of Alzheimer's Disease Subjects Using Wireless Sensor Networks

On aging there is a decrease in the cognitive functions of the brain which can result in behavioral anomalies such as wandering and susceptibility to fall, typical of patients with Alzheimers disease. In order to learn how to manage patients with cognitive impairment it is necessary to non-intrusively monitor brain activity in conjunction with body movements. To facilitate the translation of insights derived through wireless monitoring into robust strategies for crisis prevention and management, we provide a preliminary assessment of a patient monitoring infrastructure, and we discuss related issues and challenges.

A cross-layer approach for publish/subscribe in mobile ad hoc networks

In the context of ubiquitous and pervasive computing, publish/subscribe middleware is gaining momentum due to its loosely coupled communication scheme. In this paper we present Q, a publish/subscribe service conceived to operate over mobile ad hoc networks. With Q, the overlay network that routes events from publishers to subscribers dynamically adapts to topology changes by means of cross-layer interaction. Q also supports content-based filtering of events through mobile code: subscribers can specify in detail the notifications they wish to receive by defining proper filter classes, then binary code of filters is exchanged during runtime by participating nodes.

An application adaptation layer for wireless sensor networks

In wireless sensor networks, poor performance or unexpected behavior may be experienced for several reasons, such as trivial deterioration of sensing hardware, unsatisfactory implementation of application logic, or mutated network conditions. This leads to the necessity of changing the application behavior after the network has been deployed. Such flexibility is still an open issue as it can be achieved either at the expense of significant energy consumption or through software complexity. This paper describes an approach to adapt the behavior of running applications by intercepting the calls made to the operating system services and changing their effects at run-time. Customization is obtained through small fragments of interpreted bytecode, called adaptlets, injected into the network by the base station. Differently from other approaches, where the entire application is interpreted, adaptlets are tied only to specific services, while the bulk of the application is still written in native code. This makes our system able to preserve the compactness and efficiency of native code and to have little impact on the overall application performance. Also, applications must not be rewritten because the operating system interfaces are unaffected. The adaptation layer has been implemented in the context of TinyOS using an instruction set inspired to the Java bytecode. Examples that illustrate the programming of the adaptation layer are presented together with their experimental validation.

Opportunistic computing for wireless sensor networks

Wireless sensor networks are moving from academia to real world scenarios. This will involve, in the near future, the design and production of hardware platforms characterized by low-cost and small form factor. As a consequence, the amount of resources available on a single node, i.e. computing power, storage, and energy, will be even more constrained than today. This paper faces the problem of storing and executing an application that exceeds the memory resources available on a single node. The proposed solution is based on the idea of partitioning the application code into a number of opportunistically cooperating modules. Each node contributes to the execution of the original application by running a subset of the application tasks and providing service to the neighboring nodes.