Wearable Activity Tracking in Car Manufacturing
By Thomas Stiefmeier, Daniel Roggen, G. Ogris, Paul Lukowicz, Gerhard Tröster. IEEE pervasive computing, 2008
381 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/e709642b47a1dd4d39e8238ae38770d84b4a2aa1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yu Guan
- Francisco Javier Ordonez
- Andreas Bulling
- Daniel Roggen
- Thomas Plötz
- Ulf Blanke
- Bernt Schiele
- Jialiu Lin
Citations in the corpus
- The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition (2013, background): “…available at SciVerse ScienceDirect Pattern Recognition Letters journal homepage: www.elsevier .com/locate /patrec The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition Ricardo Chavarriaga a,⇑, Hesam Sagha a, Alberto Calatroni b, Sundara Tejaswi Digumarti…”
- A tutorial on human activity recognition using body-worn inertial sensors (2014, background): “Several real-world domains were identified that would clearly benefit from activity recognition, such as the industrial sector [Maurtua et al. 2007; Stiefmeier et al. 2008], office scenarios, the sports and entertainment sector [Kunze et al.”
- A framework of energy efficient mobile sensing for automatic user state recognition (2009, background): “[6] could support a production or maintenance worker by recognizing the worker’s actions and delivering just-in-time”
- Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, methodology): “We evaluate our method on three standard benchmark datasets, namely the Opportunity challenge [Chavarriaga et al. 2013], PAMAP2 [Reiss and Stricker 2012], and the Skoda dataset [Stiefmeier et al. 2008].”
- Activity recognition in beach volleyball using a Deep Convolutional Neural Network (2017, background): “2015) and for activity tracking in car manufacturing (Stiefmeier et al. 2008).”
- Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016, background): “, from simple hand gestures to complex activities, such as “cooking a meal”) and the context in which they occur from sensor data is at the core of smart assistive technologies, such as in smart homes [1], in rehabilitation [2], in health support [3,4], in skill assessment [5] or in industrial setti”