A residential maintenance-free long-term activity monitoring system for healthcare applications

Fafoutis, Xenofon; Tsimbalo, Evgeny; Mellios, Evangelos; Hilton, Geoff; Piechocki, Robert J.; Craddock, Ian J

doi:10.1186/s13638-016-0534-3

Cited by 30 publications

(30 citation statements)

References 53 publications

(58 reference statements)

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“…The fast-growing market of activity trackers provides evidence for high acceptability of such unobtrusive wrist-worn devices. Their main drawback is usually a short battery life, although some projects report their accelerometers to run for months on a single coin-cell battery [38]. Other solutions in this space, such as instrumenting subjects with multiple accelerometers or other devices, are more suited for lab settings rather than for AAL spaces.…”

Section: Locomotion Transitions and Posturementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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Section: Locomotion Transitions and Posturementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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“…3 and divided by the number of samples inside a packet; f s is the sampling frequency; and N is the number of accelerometers. In particular, we consider a scenario where SPW-1 streams raw accelerometer data using the undirected connectionless BLE advertisements (similarly to [10]). Although data reliability can be addressed at the receiver [22], this communication approach does not provide delivery guarantees and, thus, can be only applied to applications that can tolerate data loss or make use of specific missing data techniques [13].…”

Section: Energy Consumption and Battery Lifetime Estimationsmentioning

confidence: 99%

“…In particular, both wearable sensor units were mounted on a ground plane. At the other side of the room, at a distance of 4.4 m, a receiver unit with two orthogonally polarised patch antennas was used [10]. In both cases, the transmitter was programmed to transmit advertisement packets at a period of 100 ms (4 dBm transmission power).…”

Section: Wireless Performancementioning

confidence: 99%

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SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

Fafoutis

Janko

Mellios

et al. 2016

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. In this paper, we present SPW-1; a low-profile versatile wearable activity tracker that employs two ultra-low-power accelerometers and relies on Bluetooth Low Energy (BLE) for wireless communication. Aiming for a low maintenance system, SPW-1 is able to offer a battery lifetime of multiple months. Measurements on its wireless performance in a real residential environment with thick brick walls, demonstrate that SPW-1 can fully cover a room and -in most cases -the adjacent room, as well. SPW-1 is a research platform that is aimed to be used both as a data collecting tool for health-oriented studies outside the laboratory, but also for research on wearable technologies and body-centric communications. As a result, SPW-1 incorporates versatile features, such as external sensor support, various powering options, and accelerometer configuration options that can support a wide range applications from kinematics to long-term activity recognition.

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“…Instead, we assume that Eve has physical access to the common areas outside of Alice's home environment; areas that are within the wireless range of the wearable device of Alice. Indeed, it has been shown that the signal of a wearable system can traverse through multiple walls in residential environments [17]. Hence, Eve is able to eavesdrop and store the encrypted packets of the raw acceleration data, using a passive sniffer.…”

Section: Introductionmentioning

confidence: 99%

Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems

Fafoutis

Marchegiani

Papadopoulos

et al. 2017

IEEE Signal Process. Lett.

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Abstract-With the ubiquity of sensing technologies in our personal spaces, the protection of our privacy and the confidentiality of sensitive data becomes a major concern. In this paper, we focus on wearable embedded systems that communicate data periodically over the wireless medium. In this context, we demonstrate that private information about the physical activity levels of the wearer can leak to an eavesdropper through the physical layer. Indeed, we show that the physical activity levels strongly correlate with changes in the wireless channel that can be captured by measuring the signal strength of the eavesdropped frames. We practically validate this correlation in several scenarios in a real residential environment, using data collected by our prototype wearable accelerometer-based sensor. Lastly, we propose a privacy enhancement algorithm that mitigates the leakage of this private information.

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A residential maintenance-free long-term activity monitoring system for healthcare applications

Cited by 30 publications

References 53 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications

Privacy Leakage of Physical Activity Levels in Wireless Embedded Wearable Systems

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