Using a Smart Living Environment Simulation Tool and Machine Learning to Optimize the Home Sensor Network Configuration for Measuring the Activities of Daily Living of Older People
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. User’s Profile
3.2. Environment Characteristics
3.3. Sensors’ Characteristics
3.4. Data Simulation
3.5. Data Validation
3.6. ADLs Classification and Evaluation Metric
3.7. Case Study 1
3.8. Case Study 2
- -
- Identify which are the relevant ADLs to be measured for the older user.
- -
- Design different configurations of the home sensor network and recreate them in the SLE simulator.
- -
- Simulate the behaviour of the older user via the SLE tool to generate a consistent dataset of sensor activations.
- -
- Analyze the obtained dataset through ML algorithms and evaluate which configuration best measures the user’s ADLs (highest accuracy in ADLs classification).
- -
- Finally, the optimization of the home sensor network configuration is given by a cost-effectiveness analysis, in terms of ADL classification accuracy and the cost of the installed sensor network.
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ML Algorithm | Hyperparameter | Value |
---|---|---|
DT | Criterion | Gini |
Min. samples split | 2 | |
Min. samples leaf | 1 | |
Max. depth | None | |
SVM | C | 1 |
Kernel | rbf | |
Gamma | 1/n° features | |
KNN | n° of nearest neighbors | 3 |
GNB | No parameter | - |
Configurations | ML Algorithms | Precision [%] | Recall [%] | F1-Score [%] | Accuracy [%] |
---|---|---|---|---|---|
1 | DT | 98 | 98 | 98 | 98 |
SVM | 11 | 34 | 50 | 34 | |
KNN | 85 | 81 | 82 | 81 | |
GNB | 11 | 32 | 50 | 32 | |
2 | DT | 99 | 99 | 99 | 99 |
SVM | 15 | 38 | 56 | 38 | |
KNN | 58 | 76 | 87 | 76 | |
GNB | 92 | 95 | 98 | 95 | |
3 | DT | 98 | 98 | 98 | 98 |
SVM | 15 | 39 | 56 | 38 | |
KNN | 36 | 52 | 45 | 52 | |
GNB | 87 | 88 | 89 | 89 |
Configurations | ML Algorithms | Precision [%] | Recall [%] | F1-Score [%] | Accuracy [%] |
---|---|---|---|---|---|
1 | DT | 94 | 90 | 90 | 99 |
SVM | 4 | 11 | 7 | 34 | |
KNN | 70 | 69 | 66 | 94 | |
GNB | 25 | 31 | 37 | 34 | |
2 | DT | 99 | 99 | 99 | 99 |
SVM | 4 | 11 | 6 | 38 | |
KNN | 70 | 70 | 69 | 93 | |
GNB | 74 | 79 | 76 | 95 | |
3 | DT | 99 | 99 | 99 | 99 |
SVM | 40 | 6 | 8 | 40 | |
KNN | 80 | 80 | 79 | 97 | |
GNB | 82 | 86 | 83 | 90 |
Configurations | Cost [GBP] |
---|---|
1 | 900 |
2 | 1000 |
3 | 850 |
Configurations | ML Algorithms | Precision [%] | Recall [%] | F1-Score [%] | Accuracy [%] |
---|---|---|---|---|---|
1 | DT | 98 | 98 | 98 | 98 |
SVM | 10 | 31 | 48 | 31 | |
KNN | 45 | 64 | 81 | 64 | |
GNB | 37 | 56 | 77 | 56 | |
2 | DT | 91 | 91 | 91 | 91 |
SVM | 24 | 49 | 59 | 49 | |
KNN | 79 | 80 | 79 | 80 | |
GNB | 48 | 50 | 54 | 50 | |
3 | DT | 93 | 94 | 94 | 94 |
SVM | 15 | 39 | 56 | 39 | |
KNN | 46 | 65 | 81 | 65 | |
GNB | 56 | 61 | 93 | 61 |
Configurations | ML Algorithms | Precision [%] | Recall [%] | F1-Score [%] | Accuracy [%] |
---|---|---|---|---|---|
1 | DT | 94 | 94 | 94 | 97 |
SVM | 6 | 2 | 9 | 30 | |
KNN | 56 | 56 | 53 | 63 | |
GNB | 46 | 60 | 51 | 69 | |
2 | DT | 93 | 91 | 92 | 91 |
SVM | 25 | 50 | 33 | 50 | |
KNN | 60 | 75 | 83 | 80 | |
GNB | 43 | 57 | 53 | 51 | |
3 | DT | 83 | 82 | 81 | 94 |
SVM | 10 | 25 | 14 | 37 | |
KNN | 71 | 70 | 68 | 82 | |
GNB | 72 | 75 | 73 | 73 |
Configurations | Cost [GBP] |
---|---|
1 | 450 |
2 | 300 |
3 | 400 |
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Naccarelli, R.; Casaccia, S.; Pirozzi, M.; Revel, G.M. Using a Smart Living Environment Simulation Tool and Machine Learning to Optimize the Home Sensor Network Configuration for Measuring the Activities of Daily Living of Older People. Buildings 2022, 12, 2213. https://doi.org/10.3390/buildings12122213
Naccarelli R, Casaccia S, Pirozzi M, Revel GM. Using a Smart Living Environment Simulation Tool and Machine Learning to Optimize the Home Sensor Network Configuration for Measuring the Activities of Daily Living of Older People. Buildings. 2022; 12(12):2213. https://doi.org/10.3390/buildings12122213
Chicago/Turabian StyleNaccarelli, Riccardo, Sara Casaccia, Michela Pirozzi, and Gian Marco Revel. 2022. "Using a Smart Living Environment Simulation Tool and Machine Learning to Optimize the Home Sensor Network Configuration for Measuring the Activities of Daily Living of Older People" Buildings 12, no. 12: 2213. https://doi.org/10.3390/buildings12122213