User adaptation of convolutional neural network for human activity recognition
Open accessAbstract
Recently, monitoring human activities using smart-phone sensors, such as accelerometers, magnetometers, and gyro-scopes, has been proved effective to improve productivity in daily work. Since human activities differ largely among individuals, it is important to adapt their model to each individual with a small amount of his/her data. In this paper, we propose a user adaptation method using Learning Hidden Unit Contributions (LHUC) for Convolutional Neural Networks (CNN). It inserts a special layer with a small number of free parameters between each of two CNN layers and estimates the free parameters using a small amount of data. We collected smartphone data of 43 hours from 9 users and utilized them to evaluate our method. It improved the recognition performance by 3.0% from a user-independent model on average. The largest improvement among users was 13.6%.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Matsui et al. adapted activity recognition to each individual by adding hidden layers and customizing weights using a small amount of individual data.
“To address this challenge, Matsui et al. [163] adapted their activity recognition to each individual by adding a few hidden layers and customizing the weights using a small amount of individual data.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [163]
Matsui et al. showed a 3% improvement in recognition performance.
“They were able to show a 3% improvement in recognition performance.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [163]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
(2014) · Cited by 33,419 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513 · see citing works
Activity Recognition from User-Annotated Acceleration Data
Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147 · see citing works
Activity recognition using cell phone accelerometers
Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2011) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1964897.1964918 · Cited by 2,912 · cited 2× · see citing works
“Several HAR methods using machine learning have been proposed [3], [4], [5], [6].”
BackgroundMethodConvergence Results for Neural Networks via Electrodynamics
Panigrahy, R., Rahimi, A., Sachdeva, S., et al. (2018) · Leibniz international proceedings in informatics · doi:10.4230/lipics.itcs.2018.22 · Cited by 2,848 · see citing works
A Survey on Human Activity Recognition using Wearable Sensors
Lara, O. D., & Labrador, M. A. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2012.110112.00192 · Cited by 2,662 · see citing works
Leggetter, C. J., & Woodland, P. C. (1995) · Computer Speech & Language · doi:10.1006/csla.1995.0010 · Cited by 2,210 · see citing works
(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959 · see citing works
(2015) · Cited by 989 · see citing works
Convolutional Neural Networks for Human Activity Recognition using Mobile Sensors
Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014) · Proceedings of the 6th International Conference on Mobile Computing, Applications and Services · doi:10.4108/icst.mobicase.2014.257786 · Cited by 876 · see citing works
Swietojanski, P., & Renals, S. (2014) · 2014 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2014.7078569 · Cited by 218 · see citing works
Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors
Ronao, C. A., & Cho, S.-B. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-26561-2_6 · Cited by 176 · see citing works
Design considerations for the WISDM smart phone-based sensor mining architecture
Lockhart, J. W., Weiss, G. M., Xue, J. C., et al. (2011) · doi:10.1145/2003653.2003656 · Cited by 171 · see citing works
Speaker adaptation based on MAP estimation of HMM parameters
Lee, C.-H., & Gauvain, J.-L. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319368 · Cited by 142 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works
“In [184], the Short-time Fourier transform (STFT) for time-series sensor data is calculated and its power spectrum is used as the input to a CNN.”
MethodInfluentialDeep Learning for Sensor-based Human Activity Recognition
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2022) · ACM Computing Surveys · doi:10.1145/3447744 · Cited by 690 · see citing works
“ty in Activity Recognition Heterogeneity Subclass References User - Rokni et al. 2018 [124]; Chen et al. 2019 [26]; Soleimani and Nazerfard et al. 2019 [139]; Wooet al. 2016 [158]; Matsui et al. 2017 [94]; Saeedi et al. 2017 [130]; Morales et al. 2016 [99]; Jiang et al. 2018 [66]; Khan et al. 2018 [72]; Zheng et al. 2…”
BackgroundMethodDeep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., & Liu, Y. (2020) · arXiv · doi:10.48550/arxiv.2001.07416 · Cited by 314 · see citing works
“ty in Activity Recognition Heterogeneity Subclass References User - Rokni et al. 2018 [124]; Chen et al. 2019 [26]; Soleimani and Nazerfard et al. 2019 [139]; Wooet al. 2016 [158]; Matsui et al. 2017 [94]; Saeedi et al. 2017 [130]; Morales et al. 2016 [99]; Jiang et al. 2018 [66]; Khan et al. 2018 [72]; Zheng et al. 2…”
BackgroundMethodHu, R., Chen, L., Miao, S., & Tang, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25743 · Cited by 44 · see citing works
- Authors
- Matsui, S., Inoue, N., Akagi, Y., Nagino, G., & Shinoda, K.
- Affiliations
- Asahi Kasei (Japan) (JP)
- Tokyo Institute of Technology (JP)
- Venue
- 2017 25th European Signal Processing Conference (EUSIPCO)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 753-757
- OpenAlex
- W2766419757
- Cited by
- 46
- References
- 17
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 6 · 2022: 7 · 2021: 12 · 2020: 8 · 2019: 2 · 2018: 7
- FWCI
- 1.36
- Type
- conference paper
- Language
- en
- Open access
- Licence
- public-domain
- Locations
- European Signal Processing Conference (EUSIPCO) (publishedVersion)
- Tokyo Tech Research Repository (Tokyo Institute of Technology) (submittedVersion, public-domain)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- human activity recognition, user adaptation, convolutional neural networks, smartphone sensors, accelerometer, magnetometer, gyroscope, small sample learning, personalized models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
