Abstract
We present two types of sensor data analysis for medical and healthcare. One sensor dataset is collected in a hospital for medical purposes. We gathered accelerometer data and RFID data of real nursing in the hospital. We provide the real nursing dataset for mobile activity recognition which could be used for supervised machine learning, and also the big data combined with the patients’ medical records and sensors tried for 2 years. The other sensor dataset is collected in a developing country. We developed an eHealth system that comprises a set of sensor devices in an attache case. The first checkup was provided to 16,741 subjects. After 1 year, 2361 subjects participated in the second checkup, and the blood pressure of these subjects was significantly decreased (P<0. 001). Based on these results we proposed a cost-effective method using a predictor, to ensure sustainability of the program in developing countries.
| Original language | English |
|---|---|
| Title of host publication | Smart Sensors and Systems |
| Subtitle of host publication | Innovations for Medical, Environmental, and IoT Applications |
| Publisher | Springer International Publishing |
| Pages | 485-518 |
| Number of pages | 34 |
| ISBN (Electronic) | 9783319332017 |
| ISBN (Print) | 9783319332000 |
| DOIs | |
| Publication status | Published - Jan 1 2016 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science(all)
Cite this
Health sensor data analysis for a hospital and developing countries. / Nohara, Yasunobu; Inoue, Sozo; Nakashima, Naoki.
Smart Sensors and Systems: Innovations for Medical, Environmental, and IoT Applications. Springer International Publishing, 2016. p. 485-518.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Health sensor data analysis for a hospital and developing countries
AU - Nohara, Yasunobu
AU - Inoue, Sozo
AU - Nakashima, Naoki
PY - 2016/1/1
Y1 - 2016/1/1
N2 - We present two types of sensor data analysis for medical and healthcare. One sensor dataset is collected in a hospital for medical purposes. We gathered accelerometer data and RFID data of real nursing in the hospital. We provide the real nursing dataset for mobile activity recognition which could be used for supervised machine learning, and also the big data combined with the patients’ medical records and sensors tried for 2 years. The other sensor dataset is collected in a developing country. We developed an eHealth system that comprises a set of sensor devices in an attache case. The first checkup was provided to 16,741 subjects. After 1 year, 2361 subjects participated in the second checkup, and the blood pressure of these subjects was significantly decreased (P<0. 001). Based on these results we proposed a cost-effective method using a predictor, to ensure sustainability of the program in developing countries.
AB - We present two types of sensor data analysis for medical and healthcare. One sensor dataset is collected in a hospital for medical purposes. We gathered accelerometer data and RFID data of real nursing in the hospital. We provide the real nursing dataset for mobile activity recognition which could be used for supervised machine learning, and also the big data combined with the patients’ medical records and sensors tried for 2 years. The other sensor dataset is collected in a developing country. We developed an eHealth system that comprises a set of sensor devices in an attache case. The first checkup was provided to 16,741 subjects. After 1 year, 2361 subjects participated in the second checkup, and the blood pressure of these subjects was significantly decreased (P<0. 001). Based on these results we proposed a cost-effective method using a predictor, to ensure sustainability of the program in developing countries.
UR - http://www.scopus.com/inward/record.url?scp=85018866031&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85018866031&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-33201-7_18
DO - 10.1007/978-3-319-33201-7_18
M3 - Chapter
AN - SCOPUS:85018866031
SN - 9783319332000
SP - 485
EP - 518
BT - Smart Sensors and Systems
PB - Springer International Publishing
ER -