Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm
Abstract
:1. Introduction
2. Methods and Materials
2.1. Functional Specifications of a Reliable Falling Monitor
- It must guarantee a sensitivity value near 100%, with a very low rate of false alarms (high specificity), in all situations and environments where the supervised subject lives.
- The system must be emotionally accepted by target subjects. A well-known derived requirement is the unobtrusive character of the system [21].
- Personalization. The monitor should be customized to the subject under surveillance. The changing nature of human behavior and health suggests that the system must evolve with the subject. We call this an “adaptive monitor” since it is able to follow the associated subject in a continuous way.
- Physical Risk Events (PRE) detector. Falls can be classified in impact-based PREs and non-impact-based PREs. Body impacts must trigger the analysis of activity around the event, to detect impact-based PREs.
- Computational architecture functionally partitioned. A distributed processing architecture, with a division of modules on account of functional tasks, should be a solution to the current limitations on the processing capacity of unobtrusive smart monitors.
- Attention to personal preferences and needs. This requirement agrees with the necessity to allow changes in the position of the sensor, because of health conditions (e.g., dermatitis).
2.2. Laboratory Study
2.3. Accelerometer Sensor Hardware Used in the Study
2.4. Algorithms for Impact-Based PRE Detection: Definition and Evaluation
2.5. Smart Monitor Sketch
3. Results
- Personalization. It is shown that personalization improves the sensitivity and specificity of the isotropic algorithm, which can be considered a dichotomic classifier of activities (impact, no impact).
- Optimal parameter region. It is shown that there is an optimal region in the parameter space of the algorithm, Ropt, defined by a sensitivity of 100%, and a high specificity (low rate of false positives), which is robust.
- Functional partition. The analysis of the efficiency of the algorithm inside Ropt will support the functional partition of the computational architecture of the monitor.
- Reachability. It is shown that Ropt is reachable by means of an unsupervised continuous learning technique, with very low computational load, which provides the adaptive feature of the algorithm.
- Anisotropic Algorithm. We verified that the performance of the 3-axis anisotropic algorithm surpasses that of the isotropic algorithm, although it keeps the remaining properties.
3.1. Stage I. Personalization
3.2. Stage II. Optimal Parameter Region
3.3. Stage III. Functional Partition
3.4. Stage IV. Reachability
Activity | tf − ti | Em | Emmax& | EACmax | tf − ti | Em | Emmax | EACmax |
---|---|---|---|---|---|---|---|---|
Slow walking | 62.5 ± 10.7 | 0.023 ± 0.002 | 0.025 0.027 | 0.038 | 29.6 ± 19.0 | 0.038 ± 0.014 | 0.071 | 0.101 |
Normal walking | 29.1 ± 1.8 | 0.046 ± 0.004 | 0.053 0.049 | 0.082 | 21.4 ± 8.5 | 0.056 ± 0.014 | 0.089 | 0.142 |
Fast walking | 22.1 ± 1.7 | 0.103 ± 0.012 | 0.115 0.090 | 0.200 | 15.8 ± 6.3 | 0.102 ± 0.033 | 0.160 | 0.252 |
Going upstairs | 17.1 ± 1.9 | 0.048 ± 0.005 | 0.057 0.043 | 0.081 | 18.4 ± 1.7 | 0.051 ± 0.014 | 0.092 | 0.127 |
Going downstairs | 14.4 ± 2.9 | 0.094 ± 0.012 | 0.108 0.072 | 0.191 | 15.0 ± 4.1 | 0.103 ± 0.039 | 0.189 | 0.312 |
Vertical jump | 3.2 ± 0.4 | 0.130 ± 0.022 | 0.160 | 0.257 | 7.2 ± 8.6 | 0.097 ± 0.048 | 0.200 | 0.298 |
Knee falling | 2.9 ± 0.6 | 0.072 ± 0.010 | 0.081 | 0.163 | 3.9 ± 3.3 | 0.078 ± 0.034 | 0.170 | 0.239 |
Horizontal falling | 3.2 ± 1.3 | 0.115 ± 0.026 | 0.160 | 0.284 | 14.2 ± 27.7 | 0.083 ± 0.046 | 0.162 | 0.315 |
Vertical jump sf | 4.6 ± 2.0 | 0.121 ± 0.032 | 0.163 | 0.264 | 4.4 ± 2.4 | 0.0105 ± 0.027 | 0.162 | 0.282 |
Knee falling sf | 2.9 ± 1.0 | 0.096 ± 0.020 | 0.115 | 0.203 | 3.9 ± 2.6 | 0.069 ± 0.021 | 0.158 | 0.189 |
Horizontal falling sf | 3.2 ± 1.3 | 0.125 ± 0.043 | 0.203 | 0.373 | 5.4 ± 3.7 | 0.103 ± 0.048 | 0.186 | 0.380 |
3.5. Stage V. Anisotropic Algorithm
3.6. Adaptive “Divide and Conquer” Smart Monitor (DCSM)
4. Discussion
5. Conclusions
Acknowledgments
Conflicts of Interest
References
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Prado-Velasco, M.; Marín, R.O.; Del Rio Cidoncha, G. Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm. Int. J. Environ. Res. Public Health 2013, 10, 4767-4789. https://doi.org/10.3390/ijerph10104767
Prado-Velasco M, Marín RO, Del Rio Cidoncha G. Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm. International Journal of Environmental Research and Public Health. 2013; 10(10):4767-4789. https://doi.org/10.3390/ijerph10104767
Chicago/Turabian StylePrado-Velasco, Manuel, Rafael Ortiz Marín, and Gloria Del Rio Cidoncha. 2013. "Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm" International Journal of Environmental Research and Public Health 10, no. 10: 4767-4789. https://doi.org/10.3390/ijerph10104767