Learning Datum-Wise Sampling Frequency for Energy-Efficient Human Activity Recognition
Authors: Weihao Cheng, Sarah Erfani, Rui Zhang, Ramamohanarao Kotagiri
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate DWFS with three real-world HAR datasets, and the results show that DWFS statistically outperforms the state-of-the-arts regarding a combined measurement of accuracy and energy efficiency. |
| Researcher Affiliation | Academia | School of Computing and Information Systems, The University of Melbourne {weihaoc@student.,sarah.erfani@,rui.zhang@,kotagiri@}unimelb.edu.au |
| Pseudocode | Yes | Algorithm 1 DWFS Generate H; Algorithm 2 DWFS Learning; Algorithm 3 DWFS Inference |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | Human Activity Sensing Consortium (HASC) 2011 (Kawaguchi et al. 2011): The data of 6 activities was collected with 100 readings per second by 7 subjects using i Phone/i Pod. Human Activity Recognition on Smartphones Dataset (HARSD) (Anguita et al. 2013): The data of 6 activities was collected with 50 readings per second by 30 subjects using a Samsung Galaxy S II. Daily Sport Activities dataset (DSA) (Barshan and Y uksek 2013): The data of 19 activities was collected with 25 readings per second by 8 subjects using body-worn sensors (we use the data collected by the sensor placed on a subject s torso). |
| Dataset Splits | Yes | We conduct the experiments based on 5-fold cross-validation, where we take 1/5 of D in turn for testing, and the other 4/5 of D for training. |
| Hardware Specification | No | The paper mentions the operating system ('Python 2.7 on 64-bit Ubuntu 14.04 LTS operating system') but does not specify any hardware components like CPU, GPU models, or memory. |
| Software Dependencies | No | The paper mentions 'Python 2.7 on 64-bit Ubuntu 14.04 LTS operating system' but does not list any other software libraries, frameworks, or solvers with specific version numbers. |
| Experiment Setup | Yes | The iteration round L for training is set to 5. We use softmax regression as the classification model of DWFS, that the parameter θ is an m (d +1) matrix including intercepts, and we optimize θ iteratively by Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. We model the parameter ψ as a K (d + m) matrix, that ψk(s) = ψk s, and we optimize ψ iteratively via BFGS as well. We set the parameter β = 1.2 which is used to impose instance weight. |