A Long-Term Evaluation of Sensing Modalities for Activity Recognition

By Beth T. Logan, Jennifer A. Healey, Matthai Philipose, Emmanuel Munguia Tapia, Stephen S. Intille. Ubiquitous Computing, 2007

441 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/d651d682101a1d68381819137a573071729cc9e3

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In Computer Networks and Communications

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Citations in the corpus

Advanced internet of things for personalised healthcare systems: A survey (2017, background): “DT also is also a typical algorithm widely used in healthcare applications, especially for physical activity monitoring related disease diagnosis and treatments [108].”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “Accelerometer-based HAR has been deployed in a large number of domains including smart homes [5, 32, 38], health care [31, 37], daily activity tracking [29], fitness tracking [16], fall detection of elderly people [18] and transportation mode detection [9, 24, 42].”
A Survey on Ambient Intelligence in Health Care (2013, neutral): “These sensors are adept in performing location-based activity recognition in indoor environments [49], [50] and their long battery life supports long-term data collection [51].”
CASAS: A Smart Home in a Box (2012, background): “In fact, while realistic smart home prototypes have been designed [2] [3], implementing these smart homes is so cumbersome that meetings have been organized to discuss ways to scale such pervasive computing systems [4] and to share valuable data that has been successfully captured in such settings.”
Wireless sensor networks for healthcare: A survey (2010, neutral): “In a similar study [91], the authors conduct experiments on 104 hours of activity data collected from a person living in a home instrumented with over 900 sensor inputs which include built-in wired sensors, motion-detection sensors and RFID tags.”
Activity sensing in the wild: a field trial of ubifit garden (2008, background): “These emerging technologies have seen success with participants in controlled and “living” lab settings [11] and with researchers in situ [18].”
Eye Movement Analysis for Activity Recognition Using Electrooculography (2009, background): “The term “ﬁxation” can also be referred to as the time between two saccades during which the eyes are relatively stationary.”
Transfer Learning for Activity Recognition: A Survey (2013, background): “These sensors are adept in performing location-based activity recognition in indoor environments [1,27,38] just as GPS is used for outdoor environments [36].”
Unintrusive eating recognition using Google Glass (2015, methodology): “[10] used more than 900 environmental sensors tagged with objects and achieved AUC of 0.”
Activity Recognition on Streaming Sensor Data (2012, methodology): “Environment sensors such as infra red based motion detectors or reed switches based door sensors have also been used for gathering information about a more general set of ADLs such as cook, leave home, sleep, eat, etc; as explored by others [17][18][19][20].”