Matey-Sanz, Miguel; Casteleyn, Sven; Granell-Canut, Carlos Dataset of inertial measurements of smartphones and smartwatches for human activity recognition Journal Article Data in Brief, 51 , pp. 109809, 2023, ISSN: 2352-3409. Abstract | Links | BibTeX @article{Matey2023c,
title = {Dataset of inertial measurements of smartphones and smartwatches for human activity recognition},
author = {Miguel Matey-Sanz and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1016/j.dib.2023.109809},
issn = {2352-3409},
year = {2023},
date = {2023-12-15},
journal = {Data in Brief},
volume = {51},
pages = {109809},
abstract = {This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches. |
Gómez-Cambronero, Águeda "Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms PhD Thesis Universitat Jaume I. INIT, 2023. Abstract | Links | BibTeX @phdthesis{GomezCambronero2023b,
title = {"Horizon: Resilience": A Smartphone-based Serious Game Intervention for Depressive Symptoms},
author = {Águeda Gómez-Cambronero},
url = {http://hdl.handle.net/10803/689528},
doi = {http://dx.doi.org/10.6035/14101.2023.544418},
year = {2023},
date = {2023-12-11},
school = {Universitat Jaume I. INIT},
abstract = {Depression is the most prevalent mental issue in our society, leading to disability and suicide deaths. The COVID-19 pandemic has intensified the need for depression treatment and prevention. While effective, evidence-based psychological treatments for depression exists, only a small percentage of those in need actually receive them. Technology, particularly smartphone-based interventions, can help maximize the reach of these treatments while ensuring their effectiveness, although it comes with challenges, such as high dropout rates. Despite the potential
of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.
This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an
intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness-},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Depression is the most prevalent mental issue in our society, leading to disability and suicide deaths. The COVID-19 pandemic has intensified the need for depression treatment and prevention. While effective, evidence-based psychological treatments for depression exists, only a small percentage of those in need actually receive them. Technology, particularly smartphone-based interventions, can help maximize the reach of these treatments while ensuring their effectiveness, although it comes with challenges, such as high dropout rates. Despite the potential
of this therapy, this is a field that requires considerably more research to fully explore the benefits that smartphones have to offer. Specifically, serious games, designed with a purpose beyond entertainment, have emerged as a promising treatment tool, leveraging advance smartphone capabilities, aligning with psychological treatment principles, and enhancing user engagement.
This dissertation introduces “Horizon: Resilience”, a smartphone-based Serious Game for depressive symptoms. It is a city builder game with a decision making narrative, in which the player (patient) manages a town. The objective is to make the town progress, ensuring the steady inflow of resources and fostering the psychological resilience of its inhabitants. The game is based on the Cognitive Behavioral Therapy (CBT) framework and includes Positive Psychology (PP) techniques. These psychological techniques are woven into the game’s gameplay, feedback, economy system, quests, graphics, and story. Noteworthy is the integration of promoting Physical Activity, detected using the phone’s motion sensors, as part of gameplay. The game draws on the findings of a scoping review on smartphone-based serious games in mental health, and was informed by consultations with therapists as part of a user-centered design. Therapists and patients furthermore provided a qualitative and quantitative evaluation of a Minimum Viable Product (MVP) of the game. Their positive impressions indicate high acceptance and positive expectation regarding the use of the game as an
intervention. Lastly, a pilot randomized controlled trial protocol is outlined to assess its preliminary effectiveness- |
Matey-Sanz, Miguel; Torres-Sospedra, Joaquín; González-Pérez, Alberto; Casteleyn, Sven; Granell-Canut, Carlos Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition Inproceedings Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 391–405, Springer, Cham, 2023, ISBN: 978-3-031-49018-7. Abstract | Links | BibTeX @inproceedings{Matey2023b,
title = {Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition},
author = {Miguel Matey-Sanz and Joaquín Torres-Sospedra and Alberto González-Pérez and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1007/978-3-031-49018-7_28},
isbn = {978-3-031-49018-7},
year = {2023},
date = {2023-12-01},
booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
volume = {14469},
pages = {391–405},
publisher = {Springer, Cham},
series = {Lecture Notes in Computer Science},
abstract = {The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data. |
González-Pérez, Alberto Applying Mobile and Geospatial Technologies to Ecological Momentary Interventions PhD Thesis Universitat Jaume I. INIT, 2023. Abstract | Links | BibTeX @phdthesis{Gonzalez-Perez2023b,
title = {Applying Mobile and Geospatial Technologies to Ecological Momentary Interventions},
author = {Alberto González-Pérez},
doi = {http://dx.doi.org/10.6035/14101.2023.533823},
year = {2023},
date = {2023-09-07},
school = {Universitat Jaume I. INIT},
abstract = {Today a large percentage of the population suffers from anxiety-related problems. This anxiety can appear in day-to-day situations. An effective therapy for these problems is exposure. In it, the person is gradually exposed to what he fears. However, these therapy sessions are long and force the patient and therapist to travel to a specific place. Here, the use of a mobile application that guides the patient during the exposure sessions can be beneficial. Until now, this application did not exist, due to the complexity of its implementation. In this doctoral thesis, the necessary tools have been implemented to facilitate the implementation of this type of solution. In addition, in collaboration with psychology professionals, a mobile application has been implemented to self-guide exposure, which has been positively assessed by an external committee of experts.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Today a large percentage of the population suffers from anxiety-related problems. This anxiety can appear in day-to-day situations. An effective therapy for these problems is exposure. In it, the person is gradually exposed to what he fears. However, these therapy sessions are long and force the patient and therapist to travel to a specific place. Here, the use of a mobile application that guides the patient during the exposure sessions can be beneficial. Until now, this application did not exist, due to the complexity of its implementation. In this doctoral thesis, the necessary tools have been implemented to facilitate the implementation of this type of solution. In addition, in collaboration with psychology professionals, a mobile application has been implemented to self-guide exposure, which has been positively assessed by an external committee of experts. |
Gómez-Cambronero, Águeda; Casteleyn, Sven; Bretón-López, Juana; García-Palacios, Azucena; Mira, Adriana A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial Journal Article Internet Interventions, 32 , pp. 100624, 2023, ISSN: 2214-7829. Abstract | Links | BibTeX @article{GomezCambronero2023a,
title = {A smartphone-based serious game for depressive symptoms: Protocol for a pilot randomized controlled trial},
author = {Águeda Gómez-Cambronero and Sven Casteleyn and Juana Bretón-López and Azucena García-Palacios and Adriana Mira},
doi = {https://doi.org/10.1016/j.invent.2023.100624},
issn = {2214-7829},
year = {2023},
date = {2023-04-28},
journal = {Internet Interventions},
volume = {32},
pages = {100624},
abstract = {Background
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Depression is the most prevalent mental disorder, with detrimental effects on the patient's well-being, high disability, and a huge associated societal and economic cost. There are evidence-based treatments, but it is difficult to reach all people in need. Internet-based interventions, and more recently smartphone-based interventions, were explored to overcome barriers to access. Evidence shows them to be effective alternatives to traditional treatments. This paper presents the protocol of a pilot study whose primary aim is to investigate the efficacy of a smartphone-based serious game intervention for patients with mild to moderate depressive symptoms.
Methods
This randomized controlled pilot trial protocol foresees two arms design: 1/ smartphone- based serious game intervention (based on Cognitive Behavior Therapy with particular emphasis on Behavioral Activation and Physical Activity), 2/ waiting list control group. The study is expected to recruit 40 participants (18+), which will be randomly assigned to one of the experimental conditions. The duration of the intervention is two months. The primary outcome measure will be depressive symptomatology. Secondary outcomes will include other variables such as physical activity, resilience, anxiety, depression impairment, and positive and negative affect. Treatment expectation, satisfaction, usability, and game playability will also be measured. The data will be analyzed based on the intention-to-treat and per protocol analyses.
Discussion
The study aims to establish initial evidence for the efficacy of a smartphone-based serious game intervention, to serve as input for a larger-scale randomized control trial. The intervention exploits advanced smartphone capabilities, such as the use of a serious game as delivery mode, with the potential benefit of engagement and treatment adherence, and motion sensors to monitor and stimulate physical activity. As a secondary objective, the study aims to gather initial evidence on the user's expectations, satisfaction, usability and playability of the serious game as a treatment. |