Recognizing daily activities with RFID-based sensors

Buettner, Michael; Prasad, Richa; Philipose, Matthai; Wetherall, David

doi:10.1145/1620545.1620553

Cited by 252 publications

(131 citation statements)

References 24 publications

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“…Dense sensing (RFID) [92]; WISP [19] Smoking(3) Air quality [35]; aerosol detector [115] It is not easy to see what the anticipated solutions for monitoring physiological signs are. Apart from the technologies listed in the Other Sensors & Software in Table 8 only sophisticated vision-based systems are capable of detecting most of these sub-tasks -with the exception of Blood Pressure and Glucose.…”

Section: Physiological Health Condition and Healthcarementioning

confidence: 99%

“…Ear-attached mic. (chewing) [5]; surface EMG+mic (swallowing detection) [5]; dense sensing (RFID) [88], [92]; AGC [5], [133]; WISP [19] Web cams [63] Drinking(3) 5xA B [9] Dense sensing (RFID) [88], [92]; AGC [5], [133] Web cams [63] 3.3.9. Home Management, Sleep and Work Out of all the activities listed in Table 10, Sleep and Work are the most important in the context of healthy living.…”

Section: Meal Prep Dishwashing Eating and Drinkingmentioning

confidence: 99%

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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: Physiological Health Condition and Healthcarementioning

confidence: 99%

Section: Meal Prep Dishwashing Eating and Drinkingmentioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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“…Buettner et al [7] use movement data to determine that objects are used and how these are used. The movement data are collected by means of RFID technology, but unlike the normal RFID tags, these so-called Wireless Identification and Sensing Platforms (WISPs) [24] include processing and sensing capabilities, such as an accelerometer.…”

Section: Related Workmentioning

confidence: 99%

A study on automatic recognition of object use exploiting motion correlation of wireless sensors

Bosch

Marin-Perianu

Havinga

et al. 2011

Pers Ubiquit Comput

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An essential component in the ubiquitous computing vision is the ability of detecting with which objects the user is interacting during his or her activities. We explore in this paper a solution to this problem based on wireless motion and orientation sensors (accelerometer and compass) worn by the user and attached to objects. We evaluate the performance in realistic conditions, characterized by limited hardware resources, measurement noise due to motion artifacts and unreliable wireless communication. We describe the complete solution, from the theoretical design, going through simulation and tuning, to the full implementation and testing on wireless sensor nodes. The implementation on sensor nodes is lightweight, with low communication bandwidth and processing needs. Compared to existing work, our approach achieves better performance (higher detection accuracy and faster response times), while being much more computationally efficient. The potential of the concept is further illustrated by means of an interactive multi-user game. We also provide a thorough discussion of the advantages, limitations and trade-offs of the proposed solution.

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“…Buettner [7] evaluate RFID sensor networks for activity recognition, they prototyped a system that gathers object-use data in an apartment from WISPs (Wireless Identification and Sensing Platforms) and then infers daily activities with a simple Hidden Markov Model (HMM). Fused data from multiple sensors provides several advantages over data from a single sensor.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Signal based Situation Recognition with Bayesian Networks

Kim¹,

Jang²,

Jung³

et al. 2014

Journal of Electrical Engineering and Technology

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-In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

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Recognizing daily activities with RFID-based sensors

Cited by 252 publications

References 24 publications

Classification and suitability of sensing technologies for activity recognition

Classification and suitability of sensing technologies for activity recognition

A study on automatic recognition of object use exploiting motion correlation of wireless sensors

Multi-Sensor Signal based Situation Recognition with Bayesian Networks

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