Paolo Corsini

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

A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm

Abstract.In this paper, we show how one can take advantage of the stability and effectiveness of object data clustering algorithms when the data to be clustered are available in the form of mutual numerical relationships between pairs of objects. More precisely, we propose a new fuzzy relational algorithm, based on the popular fuzzy C-means (FCM) algorithm, which does not require any particular restriction on the relation matrix. We describe the application of the algorithm to four real and four synthetic data sets, and show that our algorithm performs better than well-known fuzzy relational clustering algorithms on all these sets.

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.

One-dimensional and two-dimensional generalised discrete fourier transforms

One-dimensional and two-dimensional generalized discrete Fourier transforms (GFT) are introduced. If a one-dimensional vector A is fractured into a two-dimensional matrix B, a one-dimensional GFT on A and a two-dimensional GFT on B give the same result and require the same number of operations to be computed. The result holds also for the DFT, as it is a particular case of the GFT.

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.

Graphical design of distributed applications through reusable components

The Tracs graphical programming environment promotes a modular approach to the development of distributed applications. A few types of reusable design components make the environment both simple and powerful. Tracs exploits modularity in an original way. Its support of message models, task models, and architecture models as basic design components provides programmers with a framework that has proven practical, powerful, and easy to understand. Furthermore, modularity has allowed us to add advanced facilities to the environment, with little implementation and integration effort. From this point of view, our choice of supporting message models as a basic design component has proven appropriate. Several of the ideas explored in Tracs will be useful in future work on programming environments for parallel and distributed systems. >

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.

A fuzzy relational clustering algorithm based on a dissimilarity measure extracted from data

One of the critical aspects of clustering algorithms is the correct identification of the dissimilarity measure used to drive the partitioning of the data set. The dissimilarity measure induces the cluster shape and therefore determines the success of clustering algorithms. As cluster shapes change from a data set to another, dissimilarity measures should be extracted from data. To this aim, we exploit some pairs of points with known dissimilarity value to teach a dissimilarity relation to a feed-forward neural network. Then, we use the neural dissimilarity measure to guide an unsupervised relational clustering algorithm. Experiments on synthetic data sets and on the Iris data set show that the relational clustering algorithm based on the neural dissimilarity outperforms some popular clustering algorithms (with possible partial supervision) based on spatial dissimilarity.