NDLI: Hierarchical linear models for energy prediction using inertial sensors: a comparative study for treadmill walking

Content Provider	Springer Nature Link
Author	Vathsangam, Harshvardhan Emken, B. Adar Schroeder, E. Todd Spruijt Metz, Donna Sukhatme, Gaurav S.
Copyright Year	2012
Abstract	Walking is a commonly available activity to maintain a healthy lifestyle. Accurately tracking and measuring calories expended during walking can improve user feedback and intervention measures. Inertial sensors are a promising measurement tool to achieve this purpose. An important aspect in mapping inertial sensor data to energy expenditure is the question of normalizing across physiological parameters. Common approaches such as weight scaling require validation for each new population. An alternative is to use a hierarchical approach to model subject-specific parameters at one level and cross-subject parameters connected by physiological variables at a higher level. In this paper, we evaluate an inertial sensor-based hierarchical model to measure energy expenditure across a target population. We first determine the optimal movement and physiological features set to represent data. Periodicity based features are more accurate (p < 0.1 per subject) when generalizing across populations. Weight is the most accurate parameter (p < 0.1 per subject) measured as percentage prediction error. We also compare the hierarchical model with a subject-specific regression model and weight exponent scaled models. Subject-specific models perform significantly better (p < 0.1 per subject) than weight exponent scaled models at all exponent scales whereas the hierarchical model performed worse than both. However, using an informed prior from the hierarchical model produces similar errors to using a subject-specific model with large amounts of training data (p < 0.1 per subject). The results provide evidence that hierarchical modeling is a promising technique for generalized prediction energy expenditure prediction across a target population in a clinical setting.
Starting Page	747
Ending Page	758
Page Count	12
File Format	PDF
ISSN	18685137
Journal	Journal of Ambient Intelligence and Humanized Computing
Volume Number	4
Issue Number	6
e-ISSN	18685145
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2012-07-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Accelerometer Bayesian linear regression Gyroscope Hierarchical linear model Computational Intelligence Artificial Intelligence (incl. Robotics) Robotics and Automation User Interfaces and Human Computer Interaction
Content Type	Text
Resource Type	Article
Subject	Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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