Fall detection using ultra-wideband positioning

Falls are a major health problem in our aging society. Fall detection systems are aimed at automatically sending an alarm in case of falls. Unfortunately most of the systems currently available, which use accelerometric sensors, are characterized by a relatively large number of false alarms. In fact, many activities of daily living may produce fall-like acceleration signals. We propose a method that uses ultra-wideband positioning to track the movements of the user and detect falls. Preliminary results show that the approach is reliable in detecting falls and simple postures

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

Improving the Performance of Fall Detection Systems through Walk Recognition

Social problems associated with falls of elderly citizens are becoming increasingly important because of the continuous growth of aging population. Automatic fall detection systems represent a possible answer to some of these problems, as they are useful to obtain help in case of serious injuries and to reduce the long-lie problem. Nevertheless, widespread adoption of these systems is strongly influenced by their usability and trustworthiness, which are at the moment not excellent. In fact, the user is forced to wear the device according to placement and orientation restrictions that depend on the considered fall-recognition technique. Also, the number of false alarms generated is too high to be acceptable in real world scenarios. This paper presents a technique, based on walk recognition, that increases significantly both usability and trustworthiness of a smartphone-based fall detection system. In particular, the proposed technique automatically and dynamically determines the orientation of the device, thus relieving the user from the burden of wearing the device with predefined orientation. Orientation is then used to infer posture and eliminate a large fraction of false alarms (98 %)

Posture Recognition Using the Interdistances Between Wearable Devices

Recognition of user's postures and activities is particularly important, as it allows applications to customize their operations according to the current situation. The vast majority of available solutions are based on wearable devices equipped with accelerometers and gyroscopes. In this article, a different approach is explored: The posture of the user is inferred from the interdistances between the set of devices worn by the user. Interdistances are first measured by using ultra-wideband transceivers operating in two-way ranging mode and then provided as input to a classifier that estimates current posture. An experimental evaluation shows that the proposed method is effective (up to ∼98.2% accuracy), especially when using a personalized model. The method could be used to enhance the accuracy of activity recognition systems based on inertial sensors

Smartphone-based geolocation of Internet hosts

The location of Internet hosts is frequently used in distributed applications and networking services. Examples include customized advertising, distribution of content, and position-based security. Unfortunately the relationship between an IP address and its position is in general very weak. This motivates the study of measurement-based IP geolocation techniques, where the position of the target host is actively estimated using the delays between a number of landmarks and the target itself. This paper discusses an IP geolocation method based on crowdsourcing where the smartphones of users operate as landmarks. Since smartphones rely on wireless connections, a specific delay-distance model was derived to capture the characteristics of this novel operating scenario

Tandem repeats discovery service (TReaDS) applied to finding novel cis-acting factors in repeat expansion diseases

<p>Abstract</p> <p>Background</p> <p>Tandem repeats are multiple duplications of substrings in the DNA that occur contiguously, or at a short distance, and may involve some mutations (such as substitutions, insertions, and deletions). Tandem repeats have been extensively studied also for their association with the class of repeat expansion diseases (mostly affecting the nervous system). Comparative studies on the output of different tools for finding tandem repeats highlighted significant differences among the sets of detected tandem repeats, while many authors pointed up how critical it is the right choice of parameters.</p> <p>Results</p> <p>In this paper we present <it>TReaDS - Tandem Repeats Discovery Service</it>, a <it>tandem repeat meta search engine</it>. <it>TReaDS </it>forwards user requests to several state of the art tools for finding tandem repeats and merges their outcome into a single report, providing a global, synthetic, and comparative view of the results. In particular, <it>TReaDS </it>allows the user to (<it>i</it>) simultaneously run different algorithms on the same data set, (<it>ii</it>) choose for each algorithm a different setting of parameters, and (<it>iii</it>) obtain a report that can be downloaded for further, off-line, investigations. We used <it>TReaDS </it>to investigate sequences associated with repeat expansion diseases.</p> <p>Conclusions</p> <p>By using the tool <it>TReaDS </it>we discover that, for 27 repeat expansion diseases out of a currently known set of 29, <it>long fuzzy tandem repeats </it>are covering the expansion loci. Tests with control sets confirm the specificity of this association. This finding suggests that long fuzzy tandem repeats can be a new class of cis-acting elements involved in the mechanisms leading to the expansion instability.</p> <p>We strongly believe that biologists can be interested in a tool that, not only gives them the possibility of using multiple search algorithm at the same time, with the same effort exerted in using just one of the systems, but also simplifies the burden of comparing and merging the results, thus expanding our capabilities in detecting important phenomena related to tandem repeats.</p

TRStalker: an Efficient Heuristic for Finding NP-Complete Tandem Repeats

Genomic sequences in higher eucaryotic organisms contain a substantial amount of (almost) repeated sequences. Tandem Repeats (TRs) constitute a large class of repetitive sequences that are originated via phenomena such as replication slippage, are characterized by close spatial contiguity, and play an important role in several molecular regulatory mechanisms. Certain types of tandem repeats are highly polymorphic and constitute a fingerprint feature of individuals. Abnormal TRs are known to be linked to several diseases. Researchers in bio-informatics in the last 20 years have proposed many formal definitions for the rather loose notion of a Tandem Repeat and have proposed exact or heuristic algorithms to detect TRs in genomic sequences. The general trend has been to use formal (implicit or explicit) definitions of TR for which verification of the solution was easy (with complexity linear, or polynomial in the TR\u27s length and substitution+indel rates) while the effort was directed towards identifying efficiently the sub-strings of the input to submit to the verification phase (either implicitly or explicitly). In this paper we take a step forward: we use a definition of TR for which also the verification step is difficult (in effect, NP-complete) and we develop new filtering techniques for coping with high error levels. The resulting heuristic algorithm, christened TRStalker, is approximate since it cannot guarantee that all NP-Complete Tandem Repeats satisfying the target definition in the input string will be found. However, in synthetic experiments with 30% of errors allowed, TRStalker has demonstrated a very high recall (ranging from 100% to 60%, depending on motif length and repetition number) for the NP-complete TRs. TRStalker has consistently better performance than some stateof- the-art methods for a large range of parameters on the class of NP-complete Tandem Repeats. TRStalker aims at improving the capability of TR detection for classes of TRs for which existing methods do not perform well

Fall detection using a head-worn barometer

Falls are a significant health and social problem for older adults and their relatives. In this paper we study the use of a barometer placed at the user’s head (e.g., embedded in a pair of glasses) as a means to improve current wearable sensor-based fall detection methods. This approach proves useful to reliably detect falls even if the acceleration produced during the impact is relatively small. Prompt detection of a fall and/or an abnormal lying condition is key to minimize the negative effect on health