Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People
Abstract
:1. Introduction
2. Technological Intervention
2.1. Sensor Technology
2.2. Bed and Chair Exit Recognition Approach
3. Methods
3.1. Data Collection
3.1.1. Study Participants
3.1.2. Clinical Setting and Procedure
3.2. Data Processing
3.2.1. Feature Extraction
Instantaneous features:
Contextual information features:
- Importance of each antenna in collecting sensor observations given by the relative number of readings per antenna in the segment [41];
- Mutual information between bed and chair areas given by: , where is the indicator function and n the number of elements in the segment [41];
- IDs of antennas receiving maximum and minimum RSSI in segments;
- Displacement in the axis (Figure 3B), given by: ;
- Mean and standard deviation of acceleration readings , and ;
- Mean and standard deviation of RSSI for all antennas;
- Pearson correlation between pairs of acceleration axes;
- Standard deviation of variable frequency phase rate (VFPR) [40]; and
- Sum of modulus of constant frequency phase rate (CFPR) [40].
Inter-segment features:
- Difference of median, maximum and minimum of acceleration readings , and from consecutive segments; and
- Difference of median, maximum and minimum of RSSI per antenna from consecutive segments.
3.2.2. Activity Predictor
3.2.3. Bed and Chair Exit Recognition
3.2.4. Statistical Analysis
4. Results and Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Without Score Function | Using Score Function | |||||
---|---|---|---|---|---|---|
Room 1 (%) | Room 2 (%) | Room 1 (%) | Room 2 (%) | p-value (p) | ||
Bed Exit | Recall | <0.001 | ||||
Precision | ||||||
F-score | <0.001 | |||||
Chair Exit | Recall | <0.001 | ||||
Precision | 0.68 | |||||
F-score | 0.07 |
Room 1 (%) | Room 2 (%) | ||
---|---|---|---|
Bed Exit | Recall | 72.64 ± 8.9 | 91.91 ± 9.7 |
Precision | 43.22 ± 8.7 | 66.93 ± 13.1 | |
F-score | 53.96 ± 5.84 | 76.40 ± 8.8 | |
Chair Exit | Recall | 96.98 ± 4.9 | 61.75 ± 22.5 |
Precision | 71.13 ± 21.8 | 63.99 ± 30.1 | |
F-score | 80.51 ± 16.14 | 60.55 ± 24.3 |
Room 1 | Room 2 | ||
---|---|---|---|
Bed Exit | Mean±STD | 2.63 ± 4.08 s | 3.22 ± 6.05 s |
Median | 1.20 s | 1.25 s | |
Chair Exit | Mean±STD | 1.93 ± 2.55 s | 2.15 ± 1.56 s |
Median | 1.13 s | 0.00 s |
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Shinmoto Torres, R.L.; Visvanathan, R.; Hoskins, S.; Van den Hengel, A.; Ranasinghe, D.C. Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People. Sensors 2016, 16, 546. https://doi.org/10.3390/s16040546
Shinmoto Torres RL, Visvanathan R, Hoskins S, Van den Hengel A, Ranasinghe DC. Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People. Sensors. 2016; 16(4):546. https://doi.org/10.3390/s16040546
Chicago/Turabian StyleShinmoto Torres, Roberto Luis, Renuka Visvanathan, Stephen Hoskins, Anton Van den Hengel, and Damith C. Ranasinghe. 2016. "Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People" Sensors 16, no. 4: 546. https://doi.org/10.3390/s16040546