Human activity recognition from accelerometer data using Convolutional Neural Network

By Song-Mi Lee, S. Yoon, Heeryon Cho. International Conference on Big Data and Smart Computing, 2017

356 citations recorded by the source corpus.

How researchers see this paper

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Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things (2020, methodology): “49,52,54,56 11-13 With the development of various sensing mechanisms, the design of wearable electronics is also moving toward huge amounts of data points and high-level features with significant complexity.”
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes (2021, methodology): “Lee [25] 1D-CNN 92.71% 2017 Hernandez [45] Bidir-LSTM 92.67% 2019 Mutegeki [31] CNN-LSTM 92.13% 2020 Ni [54] SDAE 97.15% 2020 The proposed approach 4-layer CNN-LSTM 99.39% -”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, background): “Therefore, the use of wearables in fitness and lifestyle has the potential to significantly advance one of the most prolific aspects of HAR applications [68, 136, 138, 164, 169, 233, 293, 295, 318].”
Deep Learning for Sensor-based Human Activity Recognition (2020, background): “ sources at the beginning, irrespective of sensing modalities. It is attractive in terms of simplicity as a strategy though it is at risk of missing detailed correlations. A simple fusion approach in [82] transformed the raw x, y, and z acceleration data into a magnitude vector by calculating the Eu”