RFID-based techniques for human-activity detection

Smith, Joshua R.; Fishkin, Kenneth P.; Jiang, Bing; Mamishev, A.V.; Philipose, Matthai; Rea, A.; Roy, Sumit; Sundara-Rajan, Kishore

doi:10.1145/1081992.1082018

Cited by 186 publications

(74 citation statements)

References 4 publications

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“…Past work with RFID in pervasive computing [14,16,20] has demonstrated the promise of more user-oriented RFIDbased applications. Unfortunately, the majority of these studies have been conducted in settings that are too restricted to bring forth the real challenges and subtleties of pervasive RFID in everyday life.…”

Section: Related Workmentioning

confidence: 99%

“…Early successes in the asset tracking and supply-chain domains [15] coupled with the falling cost of tags have lead researchers to consider pervasive, public RFID deployments that support more user-oriented services. A number of investigations into personnel tracking and task automation using RFID [2,11,14] have shown the technology's potential to facilitate everyday life by seamlessly integrating the virtual and physical worlds. Unfortunately, the majority of such studies have been limited to technology and user evaluations over a short time in restricted scenarios (often in a laboratory).…”

Section: Introductionmentioning

confidence: 99%

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Challenges for Pervasive RFID-Based Infrastructures

Welbourne

Balazinska

Borriello

et al. 2007

Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07)

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The success of RFID in supply chain management is leading many to consider more personal and pervasive deployments of this technology. Unlike industrial settings, however, deployments that involve humans raise new and critical problems related to privacy, security, uncertainty, and a more diverse and evolving set of applications.At the University of Washington, we are deploying a building-wide RFID-based infrastructure with hundreds of antennas and thousands of tags. Our goal is to uncover the issues of pervasive RFID deployments and devise techniques for addressing these issues before such deployments become common place.In this paper, we present the challenges encountered and lessons learned during a smaller-scale pilot deployment of the system. We show some preliminary results and, for each challenge, discuss how we addressed it or how we are planning on addressing it.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Challenges for Pervasive RFID-Based Infrastructures

Welbourne

Balazinska

Borriello

et al. 2007

Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07)

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“…RFID technology is attractive for many applications since it can detect people's activities by simple ID and distance information [7]. But it is difficult to detect motion.…”

Section: Introductionmentioning

confidence: 99%

Automatic Recognition of Finger Gestures Using a Tri-Axis Accelerometer

Zhou¹,

Lei²,

Cheng³

2012

IJCTE

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Abstract-More and more researchers devote themselves to system development of life support for elders such as health condition monitoring, appliances control, emergency call, and so on. Activity recognition using wearable devices is one of the ways of system implementation. In this paper, a system of gesture recognition is designed and implemented based on a tri-axis accelerometer. Six pairs of one-stroke finger gestures are defined according to the sensing characteristic of the accelerometer. Acceleration data was collected from 20 subjects under the supervision by researchers. Gesture information was segmented automatically from successive time signal. Mean, standard deviation, and the relationship of position sequence of wave crest and trough are extracted as features for training and testing of gestures classifying. Several classifiers were selected for gestures recognition. Results show the KNN classifier got the best recognizing result with an average accuracy of 91.58%. The recognition rate is not influenced by which hand being used to perform the gestures.

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“…Researchers at Intel proposed the iBracelet and iGlove wearable system for monitoring everyday interactions with objects in the home [52,53]. The wearable bracelet was equipped with an RFID reader with a small read range, while everyday objects were tagged with cheap RFID tags, costing about 10 cents each.…”

Section: Wearable Sensingmentioning

confidence: 99%

“…The wearable bracelet was equipped with an RFID reader with a small read range, while everyday objects were tagged with cheap RFID tags, costing about 10 cents each. When a user interacts with an object such as a kettle, or the stove, the wearable RFID reader reads the unique RFID tag on the object, to recognize which object the user has interacted with [53,54]. Thus, at the cost of a more expensive wearable device, Intel researchers were able to drastically reduce the cost of individual sensors attached to objects to monitor their usage.…”

Section: Wearable Sensingmentioning

confidence: 99%

Non-Invasive Sensor Solutions for Activity Recognition in SmartHomes

Srinivasan

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Smart home sensor systems that infer residents' activities enable a number of exciting medical monitoring and energy conservation applications. Existing home activity recognition systems are invasive, since they require significant manual effort from the end user in installing or training the system, inconvenience the resident by requiring them to constantly wear tags, or require invasive cameras or expensive sensors. Our main hypothesis is that by effectively using data fusion techniques, leveraging the existing smart meter infrastructure in homes, and using only weak biometric sensing, we can build convenient, accurate home activity recognition solutions for the end user.The key challenges for home activity recognition systems addressed in this dissertation include reducing configuration effort from the end user, reducing sensor installation effort, and identifying residents in multi-person homes without using invasive sensors. To reduce user configuration effort, we develop an unsupervised activity recognition algorithm called AUTOLABEL that leverages data fusion and cross-home activity models to accurately recognize resident activities without user training. To eliminate many direct sensors in the home for activity recognition, we develop effective Bayesian data fusion techniques, which combine the existing smart meter infrastructure in homes, with one low cost, non-invasive sensor per room. In our WaterSense approach, we combine a single smart water meter per home, with an occupancy sensor per room to eliminate direct sensors on individual water fixtures. In our LightSense approach, we combine a single smart energy meter per home with a light sensor per room, to eliminate direct sensors on individual light fixtures. Finally, we propose the use of resident height, which is a weak biometric, in an effective data fusion approach, to identify residents for activity recognition in multi-person homes.We evaluate our proposed activity recognition solutions through short term prototype sensor deployments in home environments lasting from 7 to 10 days each. We show that our low cost, ii convenient solutions satisfy the activity recognition needs of numerous smart home applications, such as remote medical monitoring for elderly residents, and fine-grained resource consumption monitoring of light and water fixtures in the home. Finally, we observe that our unsupervised activity recognition algorithm can be used in a wireless snoop attack on smart homes, to infer the residents' daily activities with high accuracy in spite of encrypted wireless transmissions. We propose and evaluate a suite of privacy solutions to mitigate the inference accuracy of such an attack without affecting the performance or functionality of the home activity recognition system. iv

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RFID-based techniques for human-activity detection

Abstract: The iBracelet and the Wireless Identification and Sensing Platform promise the ability to infer human activity directly from sensor readings.

Cited by 186 publications

References 4 publications

Challenges for Pervasive RFID-Based Infrastructures

Challenges for Pervasive RFID-Based Infrastructures

Automatic Recognition of Finger Gestures Using a Tri-Axis Accelerometer

Non-Invasive Sensor Solutions for Activity Recognition in SmartHomes

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