In recent years the need for technology systems composed by sensor network and artificial intelligence (AI) to measure people well-being has risen dramatically in the healthcare sector with a rapid growth in the aging population. In order to gather knowledge about human health and well-being, the capacity to quantify and measure the activities of individuals in their home environment with sensors has become an important factor. Hence combination of using information obtained from smart homes and analyzing them with AI based algorithms would be great solution to keep safe independent elderly people in home environment. The objective of this thesis is to define a measurement setup to compute and distinguish abnormal behavior from normal characterized by training machine learning algorithms (in specific K-means, agglomerative and spectral algorithms) and the unlabelled data obtained from Passive Infrared (PIR) motion sensors and door sensors which are installed in voluntary participants’ houses in Italy, The Netherlands, Switzerland and Norway. Our blind approach proposes an unsupervised learning-based solution that can be seen as representing real-world use case where it is not possible to provide accurate labelling and annotation of the sensor data. To do so, first, a review of the available information is undertaken, so as to obtain information and improve awareness. Later feature engineering techniques were developed to assess other details from the document to better explain the situation. Then different clustering algorithms have been applied on datasets to detect abnormalities in the activity of daily living of the elderly, using real and simulated data, promising accuracy test and metrics results have been reported, which indicates two out of three algorithms which applied are valid to create accurate similarity group for participants. After the training machine learning, providing 1 and 0.82 F1-score (range of F1-score between 0 and 1) furthermore 100 % and 71 % sensitivity for combined datasets with simulated data for K-means and Agglomerative clustering algorithms, respectively, while spectral clustering algorithms shown only 0.75 F1- score and 61 % sensitivity for combined datasets with simulated data, also average Silhouette coefficient (range of Silhouette Coefficient between -1 and 1) obtained from spectral algorithms for combined datasets with simulated datasets is 0.04 which is much lower than Agglomerative and K-means clustering algorithms with 0.54 and 0.61, respectively. Plus, the result obtained from just real datasets shows even lower than zero which is -0.07 that make the algorithm unreliable. However , Agglomerative and K-means clustering algorithms shown acceptable Silhouette Coefficient for real datasets with 0.5 and 0.35 respectively.

In recent years the need for technology systems composed by sensor network and artificial intelligence (AI) to measure people well-being has risen dramatically in the healthcare sector with a rapid growth in the aging population. In order to gather knowledge about human health and well-being, the capacity to quantify and measure the activities of individuals in their home environment with sensors has become an important factor. Hence combination of using information obtained from smart homes and analyzing them with AI based algorithms would be great solution to keep safe independent elderly people in home environment. The objective of this thesis is to define a measurement setup to compute and distinguish abnormal behavior from normal characterized by training machine learning algorithms (in specific K-means, agglomerative and spectral algorithms) and the unlabelled data obtained from Passive Infrared (PIR) motion sensors and door sensors which are installed in voluntary participants’ houses in Italy, The Netherlands, Switzerland and Norway. Our blind approach proposes an unsupervised learning-based solution that can be seen as representing real-world use case where it is not possible to provide accurate labelling and annotation of the sensor data. To do so, first, a review of the available information is undertaken, so as to obtain information and improve awareness. Later feature engineering techniques were developed to assess other details from the document to better explain the situation. Then different clustering algorithms have been applied on datasets to detect abnormalities in the activity of daily living of the elderly, using real and simulated data, promising accuracy test and metrics results have been reported, which indicates two out of three algorithms which applied are valid to create accurate similarity group for participants. After the training machine learning, providing 1 and 0.82 F1-score (range of F1-score between 0 and 1) furthermore 100 % and 71 % sensitivity for combined datasets with simulated data for K-means and Agglomerative clustering algorithms, respectively, while spectral clustering algorithms shown only 0.75 F1- score and 61 % sensitivity for combined datasets with simulated data, also average Silhouette coefficient (range of Silhouette Coefficient between -1 and 1) obtained from spectral algorithms for combined datasets with simulated datasets is 0.04 which is much lower than Agglomerative and K-means clustering algorithms with 0.54 and 0.61, respectively. Plus, the result obtained from just real datasets shows even lower than zero which is -0.07 that make the algorithm unreliable. However , Agglomerative and K-means clustering algorithms shown acceptable Silhouette Coefficient for real datasets with 0.5 and 0.35 respectively.

Measurement of well-being in the built environment: sensor network and AI

FARSI, ARMAN
2019/2020

Abstract

In recent years the need for technology systems composed by sensor network and artificial intelligence (AI) to measure people well-being has risen dramatically in the healthcare sector with a rapid growth in the aging population. In order to gather knowledge about human health and well-being, the capacity to quantify and measure the activities of individuals in their home environment with sensors has become an important factor. Hence combination of using information obtained from smart homes and analyzing them with AI based algorithms would be great solution to keep safe independent elderly people in home environment. The objective of this thesis is to define a measurement setup to compute and distinguish abnormal behavior from normal characterized by training machine learning algorithms (in specific K-means, agglomerative and spectral algorithms) and the unlabelled data obtained from Passive Infrared (PIR) motion sensors and door sensors which are installed in voluntary participants’ houses in Italy, The Netherlands, Switzerland and Norway. Our blind approach proposes an unsupervised learning-based solution that can be seen as representing real-world use case where it is not possible to provide accurate labelling and annotation of the sensor data. To do so, first, a review of the available information is undertaken, so as to obtain information and improve awareness. Later feature engineering techniques were developed to assess other details from the document to better explain the situation. Then different clustering algorithms have been applied on datasets to detect abnormalities in the activity of daily living of the elderly, using real and simulated data, promising accuracy test and metrics results have been reported, which indicates two out of three algorithms which applied are valid to create accurate similarity group for participants. After the training machine learning, providing 1 and 0.82 F1-score (range of F1-score between 0 and 1) furthermore 100 % and 71 % sensitivity for combined datasets with simulated data for K-means and Agglomerative clustering algorithms, respectively, while spectral clustering algorithms shown only 0.75 F1- score and 61 % sensitivity for combined datasets with simulated data, also average Silhouette coefficient (range of Silhouette Coefficient between -1 and 1) obtained from spectral algorithms for combined datasets with simulated datasets is 0.04 which is much lower than Agglomerative and K-means clustering algorithms with 0.54 and 0.61, respectively. Plus, the result obtained from just real datasets shows even lower than zero which is -0.07 that make the algorithm unreliable. However , Agglomerative and K-means clustering algorithms shown acceptable Silhouette Coefficient for real datasets with 0.5 and 0.35 respectively.
2019
2021-02-22
measurement of well-being in the built environment: sensor network and AI
In recent years the need for technology systems composed by sensor network and artificial intelligence (AI) to measure people well-being has risen dramatically in the healthcare sector with a rapid growth in the aging population. In order to gather knowledge about human health and well-being, the capacity to quantify and measure the activities of individuals in their home environment with sensors has become an important factor. Hence combination of using information obtained from smart homes and analyzing them with AI based algorithms would be great solution to keep safe independent elderly people in home environment. The objective of this thesis is to define a measurement setup to compute and distinguish abnormal behavior from normal characterized by training machine learning algorithms (in specific K-means, agglomerative and spectral algorithms) and the unlabelled data obtained from Passive Infrared (PIR) motion sensors and door sensors which are installed in voluntary participants’ houses in Italy, The Netherlands, Switzerland and Norway. Our blind approach proposes an unsupervised learning-based solution that can be seen as representing real-world use case where it is not possible to provide accurate labelling and annotation of the sensor data. To do so, first, a review of the available information is undertaken, so as to obtain information and improve awareness. Later feature engineering techniques were developed to assess other details from the document to better explain the situation. Then different clustering algorithms have been applied on datasets to detect abnormalities in the activity of daily living of the elderly, using real and simulated data, promising accuracy test and metrics results have been reported, which indicates two out of three algorithms which applied are valid to create accurate similarity group for participants. After the training machine learning, providing 1 and 0.82 F1-score (range of F1-score between 0 and 1) furthermore 100 % and 71 % sensitivity for combined datasets with simulated data for K-means and Agglomerative clustering algorithms, respectively, while spectral clustering algorithms shown only 0.75 F1- score and 61 % sensitivity for combined datasets with simulated data, also average Silhouette coefficient (range of Silhouette Coefficient between -1 and 1) obtained from spectral algorithms for combined datasets with simulated datasets is 0.04 which is much lower than Agglomerative and K-means clustering algorithms with 0.54 and 0.61, respectively. Plus, the result obtained from just real datasets shows even lower than zero which is -0.07 that make the algorithm unreliable. However , Agglomerative and K-means clustering algorithms shown acceptable Silhouette Coefficient for real datasets with 0.5 and 0.35 respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12075/4363