RECORD
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
Human-Computer Interaction · Submitted 22 Jun 2014
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%.
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The abstract, sentence by sentence
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. | contribution 0.73 | 0.93 | 0.03 | asserted 0.82 |
| s1 | Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. | method 0.71 | 0.93 | 0.05 | asserted 1.00 |
| s2 | Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. | contribution 1.00 | 0.95 | 0.48 | asserted 1.00 |
| s3 | The features are statistically shown to have good separability and are also information-rich. | result 0.98 | 0.92 | 0.08 | asserted 1.00 |
| s4 | Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. | result 1.00 | 0.93 | 0.25 | asserted 1.00 |
| s5 | For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort. | interpretation 0.58 | 0.24 | 0.29 | hedged possibility 0.64 |
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The paper
Original abstract
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.
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