A Mobile Mental Health Monitoring System: A Smart Glove

Fraiwan, Luay; Basmaji, Tasnim; Hassanin, Omnia

doi:10.1109/sitis.2018.00043

Cited by 10 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Prior research also suggests that users lack understanding of privacy issues associated with mHealth technologies [9]. Although some studies adopted certain user privacy protection methods, most of them deployed a single method (eg, data encryption [15][16][17] and extracting and storing features of data instead of original content [18,19]). A number of studies have shown that users sometimes sacrifice their privacy in exchange for benefits and personalized services [20,21].…”

Section: Patient Data Privacymentioning

confidence: 99%

“…We found and reviewed 32 papers that met the aforementioned inclusion criteria. These studies [4][5][6][12][13][14][15][16][17][18][19] are summarized in Multimedia Appendix 1 along the following dimensions: the target mental diseases that the MMHS were proposed to support, types of personal data collected by the apps, and privacy protection methods deployed in these studies, if any. These studies collected a wide variety of personal data from patients, driven by the target mental diseases.…”

Section: Personal Data Collected By Existing Mmh Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study

Zhang¹,

Lim²,

Zhou³

et al. 2021

JMIR Ment Health

View full text Add to dashboard Cite

Background Mobile mental health systems (MMHS) have been increasingly developed and deployed in support of monitoring, management, and intervention with regard to patients with mental disorders. However, many of these systems rely on patient data collected by smartphones or other wearable devices to infer patients’ mental status, which raises privacy concerns. Such a value-privacy paradox poses significant challenges to patients’ adoption and use of MMHS; yet, there has been limited understanding of it. Objective To address the significant literature gap, this research aims to investigate both the antecedents of patients’ privacy concerns and the effects of privacy concerns on their continuous usage intention with regard to MMHS. Methods Using a web-based survey, this research collected data from 170 participants with MMHS experience recruited from online mental health communities and a university community. The data analyses used both repeated analysis of variance and partial least squares regression. Results The results showed that data type (P=.003), data stage (P<.001), privacy victimization experience (P=.01), and privacy awareness (P=.08) have positive effects on privacy concerns. Specifically, users report higher privacy concerns for social interaction data (P=.007) and self-reported data (P=.001) than for biometrics data; privacy concerns are higher for data transmission (P=.01) and data sharing (P<.001) than for data collection. Our results also reveal that privacy concerns have an effect on attitude toward privacy protection (P=.001), which in turn affects continuous usage intention with regard to MMHS. Conclusions This study contributes to the literature by deepening our understanding of the data value-privacy paradox in MMHS research. The findings offer practical guidelines for breaking the paradox through the design of user-centered and privacy-preserving MMHS.

show abstract

Section: Patient Data Privacymentioning

confidence: 99%

Section: Personal Data Collected By Existing Mmh Studiesmentioning

confidence: 99%

Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study

Zhang¹,

Lim²,

Zhou³

et al. 2021

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

“…Benefitted by the development of IoT technology, IoTbased sensing systems have been applied and researched to a 2 certain extent. Through the IoT technology, it is possible to collect physiological parameters, voice information and behavioral data in a natural and real-time situation [21][22][23][24][25]. The clinicians can dynamically monitor the change of the subject's mood and disease, thereby helping the subject to induce a mood and calm the situation [26].…”

Section: Introductionmentioning

confidence: 99%

Wearable Structured Mental-Sensing-Graph Measurement

Yang

Gao

Woo

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

The emotional assessment under internet of things (IoT) architecture can support researchers to establish the relationships between human social and physiological signals and emotions. In this paper, a wearable emotion sensing system is developed under narrow band internet of things (NB-IoT) wireless communication technology. The wearable sensing device integrates social linked sensors including voice, activity, and heart rate. Using this system, a dating experiment is set up to investigate multimodal factors of perception. In particular, the multimodal data are fused in a graph structure, and this further leads to a graph convolutional neural networks model for emotion evaluation and a mentalsensing-graph intelligent interpreter. Different types of mentalsensing-graphs are fused during the training stage, and the model achieves a verification accuracy of 0.93. The intrinsic relationships among the multimodal data have been captured by the subgraphs which have star-shaped structures, and the center of the subgraphs are mostly audio node. The obtained results show that the attractiveness perception of the male participants in dating is more aligned to language communication. The results also reveal that when the male participants date highly attractive women during the experiments, a significant correlation is observed between the multimodal features and the attractiveness perception levels.

show abstract

Smart bionic graspers: background study and design process

Xin-rui

Chizari

2020

Preprint

View full text Add to dashboard Cite

This paper has focused on reviewing passive bionic grasper and designing a virtual prototype using a computer modelling technique. The main aim of this study is to review existing research and compare their functionalities. This has been followed by introducing a concept design with suitable components. To start the project, generating a clear overview form the most updated and relative knowledge and information on existing designs was the intention of the study. The concept design part of this study uses an iterative process (similar to the Double Diamond Model introduced by Frances et al (2019)) including Discover, Define, Develop and Deliver to complete the design components. Following the concept design process, the detailed theoretical considerations and the features of components selection were then defined. In Develop Phase, the goal was to decide the final design and generate the computer model using SolidWorks. The fourth phase of process was Delivery leading the design evaluation and validation of the generated model or virtual prototype. By completing the process, it is possible to determine the feasibility of the design and the need for improvement. In final stage of the design, a finite element approach using SolidWorks Simulation was performed on the concept. Final design was decided after comparing the concepts in terms of several considerations. A series of simulations were performed on the design to evaluate the durability of the design and extend its functionality. The results showed that the supporting pad was robust enough when dropped down from 1.5 meters height, while the hinge which connecting the finger straps would need further improvement to avoid failure during its practical loading.

show abstract

A Mobile Mental Health Monitoring System: A Smart Glove

Cited by 10 publications

References 16 publications

Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study

Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study

Wearable Structured Mental-Sensing-Graph Measurement

Smart bionic graspers: background study and design process

Contact Info

Product

Resources

About