As people live longer, the increasing average age of the population places additional strains on our health and social services. There are widely recognised benefits to both the individual and society from supporting people to live independently for longer in their own homes. However, falls in particular have been found to be a leading cause of the elderly moving into care, and yet surprisingly preventative approaches are not in place; fall detection and rehabilitation are too late. In this paper we present FITsense, which is building a Smart Home environment to identify increased risk of falls for residents, and so allow timely interventions before falls occurs. An ambient sensor network, installed in the Smart Home, identifies low level events taking place which is analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known "risky" profiles to allow evidence-driven intervention recommendations. Human activity recognition to identify ADLs from sensor data is a key challenge. Here we compare a windowing-based and a sequence-based event representation on four existing datasets. We find that windowing works well, giving consistent performance but may lack sufficient granularity for more complex multi-part activities.
Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident's behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier.
Knowing who benefits financially from a securities trade is necessary for the detection, prosecution and deterrence of illegal securities trading. Foreign jurisdictions with banking or securities secrecy laws are frequently used as a platform for illegal activity to frustrate law enforcement. This paper considers the extent to which Canadian law gives effect to so-called foreign blocking legislation. We conclude that while Canadian law does not generally give effect to foreign blocking legislation, it imposes only limited requirements on market intermediaries to collect beneficial ownership information. Regulators are therefore left with the option of obtaining assistance from their foreign counterparts.
he use of computer-based systems to monitor area of strong interest, and a number of organizations have developed such systems. Real-time ve-. hicle systems monitoring, which can fit hand-inglove with location monitoring, is less well developed. Our requirement for vehicle data acquisition systems @AS) was to monitor and store data on driving cycle, temperatures, pressures, engine stoichiometry, etc. In the course of working with fleets, it became clear that if vehicle systems data could be transmitted to a base station in real time, could be interpreted by base station software to provide a diagnostic capability, and could be combined with a map location .display capability, then it would be of interest to a large number of fleets. The system, which has recently been developed, consists of enboard vehicle microprocessor monitoring, data reduction and transmission components, a VHF or satellite communications link, a base station signal modem, and an AT-type microcomputer for data analysis and display. This paper traces the evolution of the microcomputer-based systems monitoring of vehicles and provides some insight into the capabilities of such systems. T and display vehicle location is currently an BACKGROUNDIn the past three years, there has been a rapid expansion of interest in computer-based systems to monitor vehicle location and to display this location on a central "dispatch" video map. Systems currently available use LORAN-C, global positioning system (GPS), or differential odometers to define vehicle position at the vehicle, and back-link this location on VHF radio or cel-
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.