Activity Recognition in the Home Using Simple and Ubiquitous Sensors

By Emmanuel Munguia Tapia, Stephen S. Intille, Kent Larson. International Conference on Pervasive Computing, 2004

1,467 citations recorded by the source corpus.

In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5c165dd70c70ca4c67d7dd3c27ff5bfc84413daa

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Citations in the corpus

Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008, background): “This has been explored in a large number of domains including classifying a user’s posture [17], tracking and classifying user exercises [5], classifying a user’s interruptibility [6] and detecting a variety of daily activities within the home [26, 24].”
A survey of intrusion detection techniques for cyber-physical systems (2014, neutral): “reuse the dataset from an earlier study [Tapia et al. 2004].”
Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes (2017, methodology): “To demonstrate the efficacy of our proposed method, we used two distinct smart home datasets—MIT PlaceLab [19] and van Kasteren [20].”
Cross-domain activity recognition (2009, background): “With the proliferation of sensor technologies, the task of recognizing human’s activities from a series of low-level sensor readings has drawn many interest in AI, user modeling and ubiquitous computing areas [24, 7].”
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, methodology): “Currently used methods are mostly based on machine-learning models, such as Bayes classifiers [54], [61], Markov models [62], [63], artificial neural networks [64], [65], and support vector machines (SVMs) [66], [67], thanks to their ability to create context models from measured data and adapt acco”
Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare (2018, background): “conductance sensor [80], PPG [81], elderly monitor [82], [83], [84], [85], [86], medication management [87], [88], food”
A Survey on Ambient Intelligence in Health Care (2013, positive): “Generative probabilistic graphical models such as hidden Markov models [72], [70], [73], [74] and dynamic Bayesian networks [64], [48] have been used to model activity sequences and to smooth recognition results of an ensemble classifier [75].”
How smart are our environments? An updated look at the state of the art (2007, methodology): “[82] also employ a naive Bayes learner to identify inhabitant activity from among a set of 35 possible classes, based on collected sensor data.”
A robust human activity recognition system using smartphone sensors and deep learning (2018, background): “In last few decades, many HAR systems were surveyed [7]–[9] where the authors focused on several activities in distinguished application domains [10,11].”
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].”
SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results (2010, background): “These activities covered a large part of th e 35 included in [15] because they were more general (we acquired the activity “eating” and not “preparing a breakfa st”, “preparing a diner”, etc.”
Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support (2017, background): “Tapia and colleagues [117] provide a detailed discussion of how data”
Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions (2008, neutral): “Previous studies have applied activity recognition, e.g., for elderly care [ 9 ].”
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers (2006, background): “There is no application independent solution to this p roblem [38].”