IR Image Macine Learning for Smart Homes
Abstract: Sweden is expecting an aging population and a shortage of healthcare professionals in the near future. This amounts to problems like providing a safe and dignified life for the elderly both economically and practically. Technical solutions that contribute to safety, comfort and quick help when needed is essential for this future. Nowadays, a lot of solutions include a camera, which is effective but invasive on personal integrity. Griddy, a hardware solution containing a Panasonic Grid-EYE, an infrared thermopile array sensor, offers more integrity for the user. Griddy was developed by students in a previous project and was used for this projects data collecting. With Griddy mounted over a bed and additional software to determine if the user is on the bed or not a system could offer monitoring with little human interaction. The purpose was to determine if this system could predict human presence with high accuracy and what limitations it might have. Two data sets, a main and a variational, were captured with Griddy. The main data set consisted of 240 images with the label “person” and 240 images with the label “no person”. The machine learning algorithms used were Support Vector Machine (SVM), k-Nearest Neighbors (kNN) and Neural Network (NN). With 10-Fold Cross Validation, the highest accuracy found was for both SVM and kNN (0.99). This was verified with both algorithms accuracy (1.0) on the test set. The results for the variational data set showed lower reliability in the system when faced with variations not presented in the training, such as elevated room temperature or a duvet covering the person. More work needs to be done to expand the main data set to meet the challenge of variations.
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