Energy-efficient activity recognition via multiple time-scale analysis

Lokare, Namita; Samadi, Soheil; Zhong, Boxuan; González, Laura López; Mohammadzadeh, Farrokh; Lobatón, Edgar

doi:10.1109/ssci.2017.8285176

Cited by 4 publications

(2 citation statements)

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“…A simple example of such approximation is to decide for each activity in isolation which setting is best for that activity [ 15 , 27 ]. This is done by testing each activity against each possible setting, and determining, for example, that sampling frequency does not affect the recognition of resting , but it needs to be high to detect running .…”

Section: Related Workmentioning

confidence: 99%

A General Framework for Making Context-Recognition Systems More Energy Efficient

Janko

Luštrek

2021

Sensors

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Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.

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Section: Related Workmentioning

confidence: 99%

A General Framework for Making Context-Recognition Systems More Energy Efficient

Janko

Luštrek

2021

Sensors

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“…It is desirable to have similar activity clusters close to each other, while dissimilar activities farther away. In order to select the values of τ 1 and τ 2 , we performed an analysis similar to the one presented in our prior work [40]. In this work, a classifier is used to determine the pair (τ 1 , τ 2 ) that yields the best F1 score for activity recognition.…”

Section: Extracting Features and Windows Of Datamentioning

confidence: 99%

Activity-Aware Physiological Response Prediction Using Wearable Sensors

2017

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Prediction of physiological responses can have a number of applications in the health and medical fields. However, this can be a challenging task due to interdependencies between these responses, physical activities, environmental factors and the individual's mental state. In this work, we focus on forecasting physiological responses in dynamic scenarios where individuals are performing exercises and complex activities of daily life. We minimize the effect of environmental and physiological factors in order to focus on the effect of physical activities. In particular, we focus on forecasting heart rate and respiratory rate due to their relevance in medical and fitness training. We aim to forecast these physiological responses up to 60 s into the future, study the effect of different predictors that incorporate different sensing modalities and different amounts of historical data and analyze the performance of various strategies for prediction. Activity information is incorporated by clustering the data streams and fitting different predictive models per cluster. The effect of clustering is also studied by performing a hierarchical analysis on the clustering parameter, and we observe that activity clustering does improve the performance in our proposed methodology when predicting physiological response across modalities.

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