Multi-sensor Activity Context Detection for Wearable Computing

By Nicky Kern, Bernt Schiele, Albrecht Schmidt. EUSAI, 2003

349 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/a8b86738ee921b548c4d27a1c609ae4894d2d3d5

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Human-Computer Interaction

Citations in the corpus

Comparative study on classifying human activities with miniature inertial and magnetic sensors (2010, background): “In this way, 480(¼60 8) signal segments are obtained for each activity.”
Human Activity Recognition Using Inertial/Magnetic Sensor Units (2010, background): “The number of sensors used vary between one [8,23] to twelve [4].”
Window Size Impact in Human Activity Recognition (2014, background): “These are some of the features most widely used in activity recognition [16,29,41,44,58] for their discrimination potential and ease of interpretation in the acceleration domain.”
Using context-aware computing to reduce the perceived burden of interruptions from mobile devices (2005, background): “Fortunately, recent work suggests that a small set of sensors gathering key information for a particular domain such as the office may provide useful information about interruptibility [17] and receptiveness to context-aware information [19].”
Analyzing features for activity recognition (2005, methodology): “7 sec in [1], 1 sec in [3], ~2 sec in [8], 8 sec in [4] and 5.”
Classification Accuracies of Physical Activities Using Smartphone Motion Sensors (2012, methodology): “Many studies have indicated the superiority of using a 1-second window size [4,11,15,16]; others have used larger window sizes such as 2 seconds [9] and 10 seconds [12] to capture more cyclic patterns.”
Gesture spotting with body-worn inertial sensors to detect user activities (2008, background): “Kern et al. [19] looked at activities, such as keyboard typing, writing on a white-board and shaking hands.”
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, background): “[12] 3D acc (×12) 8 pos, mot 1M BDM: 85–89%”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “Other studies which have used the Bayesian approach are Huynh and Schiele (2006a), Kern et al (2003) and Wu et al (2007).”
An Activity Recognition System For Mobile Phones (2009, methodology): “For human activity recognition, the deployment of multiple accelerometers not only improves the results but is rather mandatory, as noted by Kern et al. [ 10 ].”
A Practical Approach to Recognizing Physical Activities (2006, background): “The [1-12] data point Figure 2 also corresponds very closely to the results we saw in row two of Table 5 as they both represent very similar tests.”
Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey (2010, neutral): “[54, 51, 58, 42, 59, 52, 61, 62] RMS [60, 59, 42] Cum.”
Activity Recognition from User-Annotated Acceleration Data (2004, methodology): “Electronic synchronization would have required wiring between the boards which, even when the wiring is carefully designed as in [14], would restrict subject movements, especially during whole body activities such as bicycling or running.”
Egocentric recognition of handled objects: Benchmark and analysis (2009, background): “In the ubiquitous and pervasive computing communities, inferring the activities of a user is an important and popular problem and has been extensively studied, often using alternative sensors such as accelerometers [14, 12] or GPS [16].”