• Corpus ID: 233210244

Description of Structural Biases and Associated Data in Sensor-Rich Environments

  title={Description of Structural Biases and Associated Data in Sensor-Rich Environments},
  author={Massinissa Hamidi and Aomar Osmani},
In this article, we study activity recognition in the context of sensor-rich environments. We address, in particular, the problem of inductive biases and their impact on the data collection process. To be effective and robust, activity recognition systems must take these biases into account at all levels and model them as hyperparameters by which they can be controlled. Whether it is a bias related to sensor measurement, transmission protocol, sensor deployment topology, heterogeneity… 




The problem of ordered matrix compression on a deep level is solved, dividing the block into sub-blocks to achieve the best compression ratio, and it is observed that the ordered Matrix compression ratio could be improved by adopting variable-shape regions, considering both horizontal- and vertical-shaped regions.

A tutorial on human activity recognition using body-worn inertial sensors

This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition using on-body inertial sensors and describes the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems.

An Efficient Approach for Assessing Hyperparameter Importance

This paper describes efficient methods that can be used to gain insight into the relationship between hyperparameter settings and performance, and demonstrates that--even in very highdimensional cases--most performance variation is attributable to just a few hyperparameters.

A survey on wireless body area networks

This paper offers a survey of the concept of Wireless Body Area Networks, focusing on some applications with special interest in patient monitoring and the communication in a WBAN and its positioning between the different technologies.

A Model of Inductive Bias Learning

Under certain restrictions on the set of all hypothesis spaces available to the learner, it is shown that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment.

Coverage protocols for wireless sensor networks: Review and future directions

This survey proposes a taxonomy for classifying coverage protocols in WSNs and discusses open issues associated with the design of realistic coverage protocols, including realistic sensing models, realistic energy consumption models, realism connectivity models and sensor localization.

Time-variant BAN channel characterization

  • R. D’ErricoL. Ouvry
  • Business
    2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications
  • 2009
An analysis on mean channel gain, slow fading and shadowing correlation is presented with emphasis on the differences given by the human body variability and the movement condition.

Emergence of multimodal action representations from neural network self-organization

Physical Human Activity Recognition Using Wearable Sensors

A review of different classification techniques used to recognize human activities from wearable inertial sensor data shows that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms.

Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition

Investigating the trade-offs between classification accuracy and energy efficiency by comparing on- and off-node schemes shows a 40% energy saving can be obtained with a 13% reduction in classification accuracy, but this performance depends heavily on the wearer's activity.