Abstract
This paper presents an investigation into the design space for real-time, wearable posture classification systems; specifically, it analyses the impact of various factors/design choices on classification accuracy when using C4.5 decision trees. The factors can be broadly divided into: 1) system factors (such as sensor sampling rate and number of sensors used) and 2) algorithm and training factors (such as quantity of training data and temporal data features used). These factors are analysed in the context of a case study involving postural activity monitoring of Explosive Ordinance Disposal (EOD) operatives. The case study involves classifying a set of eight postures commonly encountered in EOD missions: sitting, walking, crawling, laying (on all sides) and kneeling. Design guidelines and generic lessons for a wider class of applications can be drawn from the work.
| Original language | English |
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| Title of host publication | Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012 |
| Place of Publication | Piscataway, NJ |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 200-203 |
| Number of pages | 4 |
| ISBN (Print) | 9781457721779 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering - Hong Kong and Shenzhen, China Duration: 2 Jan 2012 → 7 Jan 2012 |
Other
| Other | IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering |
|---|---|
| Country/Territory | China |
| City | Hong Kong and Shenzhen |
| Period | 2/01/12 → 7/01/12 |