Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks

By Wenchao Jiang, Zhaozheng Yin. ACM Multimedia, 2015

641 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e495d0dbb754e1a1ffa92d420f69ede9134e32ed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiwen Yu
2. Bin Guo
3. Jae-Young Pyun
4. S. Yoon
5. Lina Yao
6. Song-Mi Lee
7. Satya Prakash Singh
8. Yunhao Liu

In Biomedical Engineering

1. Ilaria De Munari
2. Aimé Lay-Ekuakille
3. Jae-Young Pyun
4. S. Yoon
5. Monica Mordonini
6. Song-Mi Lee
7. Satya Prakash Singh
8. Paolo Fornacciari

In Computer Vision and Pattern Recognition

1. Ilaria De Munari
2. Aimé Lay-Ekuakille
3. Zhiwen Yu
4. Bin Guo
5. Jae-Young Pyun
6. S. Yoon
7. Monica Mordonini
8. Lina Yao

Citations in the corpus

• IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment (2019, background): “The result obtained is positive when compared to the other studies discussed in Section II [8], [28]–[33] as presented in Fig.”
• 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, methodology): “ correlations of neighboring arranged modalities and thus misses the relations between the nonadjacent modalities. To solve this issue, unlike naturally organizing various data sources, Jiang and Yin [67] assembled signal sequences of different modalities into a novel arrangement where every signal ”
• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, neutral): “[99], [100], [104], [106], [108], [112], [119], [120], [125], [126], [141], [143], [146], [147], [149], [150], [153], [154], FIGURE 9.”
• Deep Recurrent Neural Networks for Human Activity Recognition (2017, methodology): “The proposed method achieved better overall accuracy than other methods, such as CNNs [28], least squares support vector machine (LS-SVM) [30], and random forest [31], as shown in Figure 9b.”
• Human Action Recognition From Various Data Modalities: A Review (2020, neutral): “into images using the technique in [342].”
• Deep Learning for Time-Series Analysis (2017, methodology): “In [18] the performance of CNNs is compared with that of LSTM for the classification of Visual and Haptic Data in a robotics setting, and in [28] the signals produced by wearable sensors are transformed into images so that Deep CNNs can be used for classification.”
• Human activity recognition from accelerometer data using Convolutional Neural Network (2017, methodology): “Jiang and Yin proposed a method of constructing a novel activity image for Deep CNN (DCNN) using the gyroscope, total acceleration and linear acceleration signals [4].”
• Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors (2020, background): “deep neural network (DNN) approaches for automatic feature learning from the complex datastructures [8], [12], [18]–[20].”