Accurate activity recognition in a home setting

By Tim van Kasteren, Athanasios K. Noulas, Gwenn Englebienne, Ben J. A. Kröse. 2008

943 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

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Citations in the corpus

A tutorial on human activity recognition using body-worn inertial sensors (2014, methodology): “…rec [Huynh et al. 2008]3 Joint boosting daily routines 4 1 88% prec, 90% rec [Blanke and Schiele 2009]4 CRF/HMM daily home activities 7 1 96%/95% [van Kasteren et al. 2008]5 Decision tree selected daily activities 20 20 84% acc [Bao and Intille 2004]6 AdaBoost+HMM selected daily activities 8…”
The smart thermostat: using occupancy sensors to save energy in homes (2010, methodology): “To illustrate the concept of a deep setback, Figure 8 shows the distributions of leave and return times for the publicly available Kasteren [25] and Tulum [31] home monitoring datasets, excluding weekends.”
Human Activity Recognition Using Recurrent Neural Networks (2017, methodology): “approaches have been explored for activity recognition, among them the majority of the techniques focuses on classication algorithms such as Naive Bayes (NB) [12], Decision Trees [13], HMM [5] , CRF [14], Nearest Neighbor (NN) [15], Support Vector Machines (SVM) [16] and dierent boosting techniques.”
Toolkit to support intelligibility in context-aware applications (2010, methodology): “Home Activity Recognizer – HMM We demonstrate explanations from an HMM model using the dataset from [21] about domestic activity, and train a HMM with a sequence length of 5 min, and 1 min per sequence step.”
A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer (2010, methodology): “Annotations were performed using a bluetooth headset combined with a speech-recognition software [28].”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “While most datasets are used to recognize physical activity or daily activities [19, 20, 30, 31, 47, 59, 61, 143, 155, 188, 228, 251, 252, 264, 279, 282, 299, 327, 335], there are also a few datasets dedicated to hand gestures [49, 156], breathing patterns [58], and car assembly line activities [316”
Creating and benchmarking a new dataset for physical activity monitoring (2012, methodology): “In this work, a new dataset for physical activity monitoring recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and performing 18 di.erent activities is created and made publicly available.”
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].”
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition (2013, background): “Another dataset recorded by van Kasteren et al. (2008) features longer recordings (month-long) but fewer sensors.”