Name
Title | ||
|---|---|---|
Multivariate Hidden Markov Models for Personal Smartphone Sensor Data: Time Series Analysis |
Abstract | ||
|---|---|---|
Smartphone-based human activity recognition (HAR) offers growing value for health research. We applied offline Hidden Markov Models (HMMs) to multivariate smartphone sensor data, classifying individual behaviour into a time series of states. We used supervised HMMs, validated using ground-truth data from a small self-report study. The HMMs achieved reasonable accuracy in classifying phone off-person vs. phone on-person, off-vehicle vs. on-vehicle, and phone off-person vs. sitting vs. standing vs. walking, for some participants. Strong evidence suggests that poor accuracy in other cases was caused by participant mislabeling, though HMM shortcomings contributed. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICHI.2017.84 | 2017 IEEE International Conference on Healthcare Informatics (ICHI) |
Keywords | Field | DocType |
smartphone,sensor,Hidden Markov Model,HAR,public health,iEpi,mHealth,human behavior | Time series,Data modeling,Activity recognition,Multivariate statistics,Computer science,Accelerometer,Speech recognition,Phone,Artificial intelligence,Hidden Markov model,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-4882-3 | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| William Stephen van der Kamp | 1 | 0 | 0.34 |
| Nathaniel D. Osgood | 2 | 23 | 9.92 |