Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers

By Jamie A. Ward, Paul Lukowicz, Gerhard Tröster, Thad Starner. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

504 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/55a482206d76cdb2eb9e66e944512b2d2d907e5a

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In Mechanical Engineering

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Citations in the corpus

Recognizing daily activities with RFID-based sensors (2009, background): “It has applications to elder care [12], smart hospitals [2], medication adherence [15], language learning [3], smart kindergartens [20], smart homes [5], and more [25].”
Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography (2009, background): “The problem of recognising physical activity in mobile situations, for example using body worn sensors, has been investigated by several researchers [17,21].”
A tutorial on human activity recognition using body-worn inertial sensors (2014, methodology): “By understanding classi.cation as a segmentation problem, further metrics were introduced that allow a more detailed performance analysis [Ward et al. 2006; Minnen et al. 2006b]: (1) Insertion (an activity segment is detected where there is none in the ground truth) and deletion (failure to…”
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, background): “Reference [35] focuses on recognizing activities characterized by a hand motion and an accompanying sound for assembly and maintenance applications.”
Vision-based hand-gesture applications (2011, background): “activities that produce some kind of sound; audio-signal analysis can therefore aid the recognition task.(52) While responsiveness, accuracy,”
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers (2016, methodology): “Worn microphones, which capture sounds produced by objects in operation, have been previously used for object recognition [51].”
Gesture spotting with body-worn inertial sensors to detect user activities (2008, unknown): “[1] [2] [3].”
Activity identification using body-mounted sensors—a review of classification techniques (2009, methodology): “In activity classification studies, HMMs have been used as a single classifier (Pober et al 2006) and as part of a twostage classification scheme (Lester et al 2006; Lester et al 2005; Ward et al 2006).”
Eye Movement Analysis for Activity Recognition Using Electrooculography (2009, background): “The term “ﬁxation” can also be referred to as the time between two saccades during which the eyes are relatively stationary.”
Window Size Impact in Human Activity Recognition (2014, background): “In the literature, diverse methods have been proposed to identify activity-transition points.”
Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey (2010, methodology): “Lester et al.[82] and Ward et al.[83] suggest personal activity recognition systems by using HMMs. Similarly, Zappi et al.[63] propose a dynamic sensor selection system used for HMM based recognition of manipulative activities of assembly line workers.”