CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition

• URL: http://arxiv.org/abs/2401.05426v2
• Date: Thu, 10 Oct 2024 15:18:58 GMT
• Title: CoSS: Co-optimizing Sensor and Sampling Rate for Data-Efficient AI in Human Activity Recognition
• Authors: Mengxi Liu, Zimin Zhao, Daniel Geißler, Bo Zhou, Sungho Suh, Paul Lukowicz,
• Abstract summary: Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with high-frequency sampling rates usually improves the results, it often leads to data inefficiency and unnecessary expansion of the ANN. We introduce a pragmatic framework for data-efficient utilization in HAR tasks, considering the optimization of both sensor modalities and sampling rate simultaneously.
• Score: 5.215675032361843
• License:
• Abstract: Recent advancements in Artificial Neural Networks have significantly improved human activity recognition using multiple time-series sensors. While employing numerous sensors with high-frequency sampling rates usually improves the results, it often leads to data inefficiency and unnecessary expansion of the ANN, posing a challenge for their practical deployment on edge devices. Addressing these issues, our work introduces a pragmatic framework for data-efficient utilization in HAR tasks, considering the optimization of both sensor modalities and sampling rate simultaneously. Central to our approach are the designed trainable parameters, termed 'Weight Scores,' which assess the significance of each sensor modality and sampling rate during the training phase. These scores guide the sensor modalities and sampling rate selection. The pruning method allows users to make a trade-off between computational budgets and performance by selecting the sensor modalities and sampling rates according to the weight score ranking. We tested our framework's effectiveness in optimizing sensor modality and sampling rate selection using three public HAR benchmark datasets. The results show that the sensor and sampling rate combination selected via CoSS achieves similar classification performance to configurations using the highest sampling rate with all sensors but at a reduced hardware cost.

Related papers

• A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks [53.022158485867536]
  We propose a dynamic sensor selection approach for deep neural networks (DNNs) It is able to derive an optimal sensor subset selection for each specific input sample instead of a fixed selection for the entire dataset. We show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit.
  arXiv  Detail & Related papers  (2023-08-16T14:04:50Z)
• A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition [63.26015736148707]
  This paper introduces a novel methodology to resolve the issue of optimal sensor placement for Human Activity Recognition. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach.
  arXiv  Detail & Related papers  (2023-07-06T10:38:14Z)
• Machine Learning Based Compensation for Inconsistencies in Knitted Force Sensors [1.0742675209112622]
  Knitted sensors frequently suffer from inconsistencies due to innate effects such as offset, relaxation, and drift. In this paper, we demonstrate a method for counteracting this by applying processing using a minimal artificial neural network (ANN) By training a three-layer ANN with a total of 8 neurons, we manage to significantly improve the mapping between sensor reading and actuation force.
  arXiv  Detail & Related papers  (2023-06-21T09:19:33Z)
• Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition [3.2319909486685354]
  A key problem holding up progress in wearable sensor-based human activity recognition is the unavailability of diverse and labeled training data. We propose an unsupervised statistical feature-guided diffusion model specifically optimized for wearable sensor-based human activity recognition. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data.
  arXiv  Detail & Related papers  (2023-05-30T15:12:59Z)
• DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing [95.18236298557721]
  We present DAPPER (Domain AdaPtation Performance EstimatoR) that estimates the adaptation performance in a target domain with unlabeled target data. Our evaluation with four real-world sensing datasets compared against six baselines shows that DAPPER outperforms the state-of-the-art baseline by 39.8% in estimation accuracy.
  arXiv  Detail & Related papers  (2021-11-22T08:49:33Z)
• SALIENCE: An Unsupervised User Adaptation Model for Multiple Wearable Sensors Based Human Activity Recognition [9.358282765566807]
  We propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model. It aligns the data of each sensor separately to achieve local alignment, while uniformly aligning the data of all sensors to ensure global alignment. Experiments are conducted on two public WHAR datasets, and the experimental results show that our model can yield a competitive performance.
  arXiv  Detail & Related papers  (2021-08-17T13:45:32Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors [7.003610369186623]
  This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. The experiments are conducted using a data-set of multi-sensor movement recordings from 7-month-old infants.
  arXiv  Detail & Related papers  (2021-07-02T14:02:05Z)
• Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity [57.275753974812666]
  Federated learning involves learning from data samples distributed across a network of clients while the data remains local. In this paper, we propose a novel straggler-resilient federated learning method that incorporates statistical characteristics of the clients' data to adaptively select the clients in order to speed up the learning procedure.
  arXiv  Detail & Related papers  (2020-12-28T19:21:14Z)
• Data-aided Sensing for Gaussian Process Regression in IoT Systems [48.523643863141466]
  We use data-aided sensing to learn data sets collected from sensors in Internet-of-Things systems. We show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing.
  arXiv  Detail & Related papers  (2020-11-23T20:59:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.