A Practical Approach to Recognizing Physical Activities

By Jonathan Lester, Tanzeem Choudhury, Gaetano Borriello. International Conference on Pervasive Computing, 2006

690 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/bbdd2c53ebe8b0a3a0c2b75c8fe1aa6681ce5da5

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “Lester et al (2006; 2005) used the HMM formulation as part of a two-layer classification for differentiating between a range of daily activities.”
Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition (2016, background): “It is of note that some studies have suggested that a satisfactory recognition rate can be achieved in accelerometer-based physical activity recognition using FFT features.”
Human Activity Recognition from Accelerometer Data Using a Wearable Device (2011, background): “State of the art classification performances ([5],[4]) ensures classification performances higher than 94% when two-stages classification pipeline are used.”
Design requirements for technologies that encourage physical activity (2006, background): “sophisticated sensors (perhaps the Intel Multi-sensor board [9]) to provide measurements of more types of activities to help solve the problem of proper credit.”
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors (2012, background): “In [13], accelerometer and microphone are identified as the two most important sensors to recognize activities including sitting, walking, walk-”
The design of eco-feedback technology (2010, background): “Contributing to this growing interest in eco-feedback technology is the parallel advancement and availability of sensing systems for environmentally related activities (e.g., human activity inference [35]) and interactive displays to feedback this data (e.g., iPods and mobile phones).”
SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization (2015, background): “These applications monitor individuals’ contexts to understand users’ activity, such as running, walking, or sitting [22], [23].”
UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems (2014, methodology): “In [8], authors categorised approaches according to the technique used for localization into several paradigms, including calibration-free localization [9], WLAN based techniques [10], Dead-reckoning [11], simultaneous localization and mapping (SLAM) [12] and multi-modal sensing [13].”
The Rise of People-Centric Sensing (2008, background): “Two main challenges facing data analysis in the people-centric sensing domain [9], and in the construction of accurate inference models (e.”
Window Size Impact in Human Activity Recognition (2014, methodology): “This way, in [13,14], the monitored volunteers are made to participate in the segmentation process by requiring them to set initial and end points in a handheld device.”
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones (2007, neutral): “11 wireless, GPS as well as a Bluetooth-based accelerometer, light/infrared sensor, and barometer [22].”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Plenty of traditional hand-crafted feature-based approaches [57], [312], [313], [314], [316], [317], [318], [319], [320] have been proposed for HAR using acceleration data.”
No need to war-drive: unsupervised indoor localization (2012, background): “Sensor fusion: Multi-modal sensing has received strong interests – [29] uses a body worn sensory board (MSB) for human activity recognition.”
Activity recognition with smartphone sensors (2014, methodology): “HMM is a natural solution to address the activity complexity by “smoothing” the error during the activity transition period[15].”
Design of an intelligible mobile context-aware application (2011, neutral): “Features extracted from the accelerometer are similar to [8]: e.”