Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques

Jänicke, Martin; Sick, Bernhard; Lukowicz, Paul; Bannach, David

doi:10.1109/sasow.2014.22

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…This article addresses the problem of a variable number of input sources and how to handle an input space that is extended with additional input sources. We contribute a realization of the concepts described in [36][37][38]. Data are captured and modeled in a generative manner, with all its enriching information at hand (uncertainty estimations, probabilistic class predictions, etc.).…”

Section: Research Gapmentioning

confidence: 99%

Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

2018

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Personal wearables such as smartphones or smartwatches are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automated actions according to the user’s needs. In this article, we focus on the creation of a self-adaptive activity recognition system based on IMU that includes new sensors during runtime. Starting with a classifier based on GMM, the density model is adapted to new sensor data fully autonomously by issuing the marginalization property of normal distributions. To create a classifier from that, label inference is done, either based on the initial classifier or based on the training data. For evaluation, we used more than 10 h of annotated activity data from the publicly available PAMAP2 benchmark dataset. Using the data, we showed the feasibility of our approach and performed 9720 experiments, to get resilient numbers. One approach performed reasonably well, leading to a system improvement on average, with an increase in the F-score of 0.0053, while the other one shows clear drawbacks due to a high loss of information during label inference. Furthermore, a comparison with state of the art techniques shows the necessity for further experiments in this area.

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Section: Research Gapmentioning

confidence: 99%

Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

2018

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“…However, these models present important limitations as they develop the selection process on the properties of the sensor data streams rather than capabilities and nature of sensors and device. More importantly, in all cases the sensor ecosystem must be known in advance, thus not supporting changeable scenarios with opportunistic additions or removals of sensor devices [13] .…”

Section: Introductionmentioning

confidence: 99%

MIMU-Wear: Ontology-based sensor selection for real-world wearable activity recognition

et al. 2017

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“…In our previous work, we investigated how on-body position and orientation of on-body sensors can be inferred [1,2], how position shifts can be tolerated [3], and how one sensor can replace another [4]. In [5], we started to work on the challenge of seamless sensor integration. More precisely, this means to answer the question how can a new sensor's data be integrated in an existing activity recognition system at runtime in order to improve this recognition process.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptation of Activity Recognition Systems to New Sensors

Bannach,

Jänicke,

Rey

et al. 2017

Preprint

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Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity recognition, where ubiquitous sensors lead to dynamically changing input spaces. Our method is a variation of well-established principles of machine learning, relying on unsupervised clustering to discover structure in data and inferring cluster labels from a small number of labeled dates in a semi-supervised manner. Elaborating the challenges, evaluations of over 3000 sensor combinations from three multiuser experiments are presented in detail and show the potential benefit of our approach.

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Self-Adapting Multi-sensor Systems: A Concept for Self-Improvement and Self-Healing Techniques

Cited by 8 publications

References 39 publications

Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models

MIMU-Wear: Ontology-based sensor selection for real-world wearable activity recognition

Self-Adaptation of Activity Recognition Systems to New Sensors

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