Gesture spotting with body-worn inertial sensors to detect user activities

By Holger Junker, Oliver Amft, Paul Lukowicz, Gerhard Tröster. Pattern Recognition, 2008

396 citations recorded by the source corpus.

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Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, background): “Activity spotting is a well-known subclass of activity recognition tasks, where the starting and finishing points of well-defined activities are detected based on sequential data [30].”
A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing (2015, background): “have shown eating gesture spotting with a measurement system comprised of five inertial sensors placed on the body (wrists, upper arms and on the upper torso) [2, 16].”
A Survey on Ambient Intelligence in Health Care (2013, neutral): “, walking, running, sitting, climbing, and falling) [40], [41], posture [42] and gestures [43], [44], [45], [46].”
Activity Recognition on Streaming Sensor Data (2012, methodology): “Some methodologies [41] adopt a two step approach for streaming activity recognition.”
Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey (2010, background): “Hidden Markov Models (HMMs) are statistical models widely used for activity recognition[51, 87].”
Comparative study on classifying human activities with miniature inertial and magnetic sensors (2010, background): “For example, although the gesture recognition problem has been well studied in computer vision [32], much less research has been done in this area with body-worn inertial sensors [33,34].”