The exponential growth of the technology industry over the past years has led to the development ... more The exponential growth of the technology industry over the past years has led to the development of a new paradigm. Pervasive Computing, which seeks to integrate computational capabilities into everyday objects so that they can communicate efficiently and perform useful tasks in a way that minimises the need for the end-users to interact with computers. Along with this paradigm, new technologies, such as wireless body area sensor network (WBASN) and robotics, have been rapidly growing. Such innovations can be used as health and wellness enablers. This paper introduces a system to support the health and wellbeing of people in both in-door and out-door environments. The system consists of an app, a robot and a remote server. The app and the robot can be connected to a wristband to monitor movements, physical activities, and heart rates. Furthermore, the app provides functions for users to record and monitor their calories intakes, and completed workouts. The robot is equipped with speech capabilities which, integrated with the emotion recognition algorithm, provide supportive feedback to the user. The proposed system represents an early step towards automated care in everyday life which opens the doors to many new scenarios, such as for elderly people who need help but live independently or also for people who would like to improve their lifestyles.
Health monitoring, rehabilitation, and fitness are just a few domains where human activity recogn... more Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest deg...
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Determining whether a subject has a gait impairment due to a disease or to the loss of muscularit... more Determining whether a subject has a gait impairment due to a disease or to the loss of muscularity due to advancing age is fundamental for an early diagnosis of musculoskeletal diseases. Parkinson's is the second most common neurodegenerative disease. The disease's most prevalent symptom is slow movement or sluggish gait, which can adversely impact the individual's quality of life. Generally, the gait analysis is carried out on long test sessions, which include for example long periods of walking, that cause inconvenience when the subjects under test have marked gait impairments. To help the diagnosis of Parkinson's disease, in this study we investigated the classification of Parkinson's disease by analysing only a few seconds of walking data using smart insoles, statistical analysis and machine learning techniques. The data from the smart insoles was assessed using correlation analysis. By creating pressure groups and analysing their values, it was found that the number of sensors could be reduced from 16 to 7. Furthermore, a feature vector representing the subject's gait was created by applying on the data a time windowing segmentation of 5 seconds and extracting six statistical features (mean, variance, skewness, kurtosis, energy and entropy). Four different models have been compared in terms of classification performance, reaching an F1-Score in the classification of patients with Parkinson's against healthy subjects, considering adult and elderly subjects as two separate classes, of 97.04% using the Random Forest. Such metric increased to 98.89%, using the K-Nearest Neighbours when healthy subjects were considered as a single class. The models' performance for each experiment was determined to be statistically equivalent, demonstrating the potential of this approach to provide the groundwork for the rapid detection of Parkinson's disease. Although the performance obtained is promising the number of subjects included in the study was fairly low, with a high bias towards the number of healthy subjects. Hence, in future work, the proposed solution will be tested on a larger cohort to ascertain its robustness.
Human Activity Recognition (HAR) is increasingly used in a variety of applications, including hea... more Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection tec...
The exponential growth of the technology industry over the past years has led to the development ... more The exponential growth of the technology industry over the past years has led to the development of a new paradigm. Pervasive Computing, which seeks to integrate computational capabilities into everyday objects so that they can communicate efficiently and perform useful tasks in a way that minimises the need for the end-users to interact with computers. Along with this paradigm, new technologies, such as wireless body area sensor network (WBASN) and robotics, have been rapidly growing. Such innovations can be used as health and wellness enablers. This paper introduces a system to support the health and wellbeing of people in both in-door and out-door environments. The system consists of an app, a robot and a remote server. The app and the robot can be connected to a wristband to monitor movements, physical activities, and heart rates. Furthermore, the app provides functions for users to record and monitor their calories intakes, and completed workouts. The robot is equipped with speech capabilities which, integrated with the emotion recognition algorithm, provide supportive feedback to the user. The proposed system represents an early step towards automated care in everyday life which opens the doors to many new scenarios, such as for elderly people who need help but live independently or also for people who would like to improve their lifestyles.
The exponential growth of the technology industry over the past years has led to the development ... more The exponential growth of the technology industry over the past years has led to the development of a new paradigm. Pervasive Computing, which seeks to integrate computational capabilities into everyday objects so that they can communicate efficiently and perform useful tasks in a way that minimises the need for the end-users to interact with computers. Along with this paradigm, new technologies, such as wireless body area sensor network (WBASN) and robotics, have been rapidly growing. Such innovations can be used as health and wellness enablers. This paper introduces a system to support the health and wellbeing of people in both in-door and out-door environments. The system consists of an app, a robot and a remote server. The app and the robot can be connected to a wristband to monitor movements, physical activities, and heart rates. Furthermore, the app provides functions for users to record and monitor their calories intakes, and completed workouts. The robot is equipped with speech capabilities which, integrated with the emotion recognition algorithm, provide supportive feedback to the user. The proposed system represents an early step towards automated care in everyday life which opens the doors to many new scenarios, such as for elderly people who need help but live independently or also for people who would like to improve their lifestyles.
Health monitoring, rehabilitation, and fitness are just a few domains where human activity recogn... more Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest deg...
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Determining whether a subject has a gait impairment due to a disease or to the loss of muscularit... more Determining whether a subject has a gait impairment due to a disease or to the loss of muscularity due to advancing age is fundamental for an early diagnosis of musculoskeletal diseases. Parkinson's is the second most common neurodegenerative disease. The disease's most prevalent symptom is slow movement or sluggish gait, which can adversely impact the individual's quality of life. Generally, the gait analysis is carried out on long test sessions, which include for example long periods of walking, that cause inconvenience when the subjects under test have marked gait impairments. To help the diagnosis of Parkinson's disease, in this study we investigated the classification of Parkinson's disease by analysing only a few seconds of walking data using smart insoles, statistical analysis and machine learning techniques. The data from the smart insoles was assessed using correlation analysis. By creating pressure groups and analysing their values, it was found that the number of sensors could be reduced from 16 to 7. Furthermore, a feature vector representing the subject's gait was created by applying on the data a time windowing segmentation of 5 seconds and extracting six statistical features (mean, variance, skewness, kurtosis, energy and entropy). Four different models have been compared in terms of classification performance, reaching an F1-Score in the classification of patients with Parkinson's against healthy subjects, considering adult and elderly subjects as two separate classes, of 97.04% using the Random Forest. Such metric increased to 98.89%, using the K-Nearest Neighbours when healthy subjects were considered as a single class. The models' performance for each experiment was determined to be statistically equivalent, demonstrating the potential of this approach to provide the groundwork for the rapid detection of Parkinson's disease. Although the performance obtained is promising the number of subjects included in the study was fairly low, with a high bias towards the number of healthy subjects. Hence, in future work, the proposed solution will be tested on a larger cohort to ascertain its robustness.
Human Activity Recognition (HAR) is increasingly used in a variety of applications, including hea... more Human Activity Recognition (HAR) is increasingly used in a variety of applications, including health care, fitness tracking, and rehabilitation. To reduce the impact on the user’s daily activities, wearable technologies have been advanced throughout the years. In this study, an improved smart insole-based HAR system is proposed. The impact of data segmentation, sensors used, and feature selection on HAR was fully investigated. The Support Vector Machine (SVM), a supervised learning algorithm, has been used to recognise six ambulation activities: downstairs, sit to stand, sitting, standing, upstairs, and walking. Considering the impact that data segmentation can have on the classification, the sliding window size was optimised, identifying the length of 10 s with 50% of overlap as the best performing. The inertial sensors and pressure sensors embedded into the smart insoles have been assessed to determine the importance that each one has in the classification. A feature selection tec...
The exponential growth of the technology industry over the past years has led to the development ... more The exponential growth of the technology industry over the past years has led to the development of a new paradigm. Pervasive Computing, which seeks to integrate computational capabilities into everyday objects so that they can communicate efficiently and perform useful tasks in a way that minimises the need for the end-users to interact with computers. Along with this paradigm, new technologies, such as wireless body area sensor network (WBASN) and robotics, have been rapidly growing. Such innovations can be used as health and wellness enablers. This paper introduces a system to support the health and wellbeing of people in both in-door and out-door environments. The system consists of an app, a robot and a remote server. The app and the robot can be connected to a wristband to monitor movements, physical activities, and heart rates. Furthermore, the app provides functions for users to record and monitor their calories intakes, and completed workouts. The robot is equipped with speech capabilities which, integrated with the emotion recognition algorithm, provide supportive feedback to the user. The proposed system represents an early step towards automated care in everyday life which opens the doors to many new scenarios, such as for elderly people who need help but live independently or also for people who would like to improve their lifestyles.
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Papers by Luigi D'Arco