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Perception and Intelligence Driven Sensing to Monitor Personal Health
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Perception and Intelligence Driven Sensing to Monitor Personal Health

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (25 October 2021) | Viewed by 18011

Special Issue Editors

Dr. Irene Cheng

E-Mail Website
Guest Editor
Faculty of Science, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: human perception; machine intelligence; multimedia computing; sensor data anlysis; feature detection; pattern recognition; Quality of Experience (QoE); HCI; data visualization; Just-Noticeable-Difference (JND); 3D TexMesh
Dr. Troy McDaniel

E-Mail Website
Guest Editor
The Polytechnic School, Arizona State University, Mesa, AZ 85212, USA
Interests: haptic interfaces; robotics; smart cities; human–computer and human–machine interactions; machine learning, especially for haptics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advancement of sensor technology, data analytics is not restricted to X-ray and MRI scans. Its scope has broadened from 1-K dimensional signal processing to multi-modality fusion and patient perception analysis, e.g. eye-gaze, gait, mood, pose and touch, for monitoring personal health and improvement of the quality of life. Connected health, e.g. using the Internet, Bluetooth and WIFI, offers better tele-health services to remote areas and provides richer information to society in general. These promises inevitably bring the challenges associated with dynamic big data, which include noise filtering, optimization, performance and accuracy evaluations. This special issue focuses on applying the latest state-of-the-art approaches in imaging and sensing, to intelligently acquire, process and analyze health patterns on different computer assisted diagnosis platforms, including but not limited to handheld, mobile and wearable devices. The goal is to help clinical experts and operators to accurately detect and diagnose symptoms faster in order to provide just-in-time treatments to patients. Topics include but are not limited to:

Sensor-based connected health

Sensor-based health data acquisition

Health data analytics 

Health pattern detection and recognition

Multi-modality health data fusion

Health data quality assessment

Health data optimization

Healthcare applications

Dr. Troy McDaniel
Dr. Irene Cheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Connected Health
  • Image & signal analytics
  • Wearable sensors
  • Handheld & mobile devices
  • Personal health
  • HCI
  • Cloud computing
  • Perception and visualization
  • Quality assessment

Published Papers (7 papers)

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Research

16 pages, 2260 KiB  
Article
Use of Robotic Platforms as a Tool to Support STEM and Physical Education in Developed Countries: A Descriptive Analysis
by Pedro Ponce, Christian Fernando López-Orozco, Germán E. Baltazar Reyes, Edgar Lopez-Caudana, Nancy Mazon Parra and Arturo Molina
Sensors 2022, 22(3), 1037; https://0-doi-org.brum.beds.ac.uk/10.3390/s22031037 - 28 Jan 2022
Cited by 7 | Viewed by 2817
Abstract
The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of interest are the predominant methodology used in Mexican schools and the technology as a barrier for attention. The [...] Read more.
The lack of interest of children at school is one of the biggest problems that Mexican education faces. Two important factors causing this lack of interest are the predominant methodology used in Mexican schools and the technology as a barrier for attention. The methodology that institutions have followed has become an issue because of its very traditional approach, with the professor giving all the theoretical material to the students while they listen and memorize the contents, and, if we add the issue of the growing access to technological devices for students, children carrying a phone are more likely to be distracted. This study aims to integrate technology through assistive robots as a beneficial tool for educators, in order to improve the attention span of students by making the learning process in multiple areas of the Mexican curriculum more dynamic, therefore obtaining better results. To prove this, four different approaches were implemented; three in elementary schools and one in higher education: the LEGO® robotic kit and the NAO robot for STEM (science, technology, engineering, and mathematics) teaching, the NAO robot for physical education (PE), and the PhantomX Hexapod, respectively. Each of these technological approaches was applied by considering both control and experimental groups, in order to compare the data and provide conclusions. Finally, this study proves that the attention span is indeed improved as a result of implementing robotic platforms during the teaching process, allowing the children to become more motivated during their PE class and become more proactive and retain more information during their STEM classes. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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21 pages, 4992 KiB  
Article
Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System
by Juana Isabel Méndez, Ana Victoria Meza-Sánchez, Pedro Ponce, Troy McDaniel, Therese Peffer, Alan Meier and Arturo Molina
Sensors 2021, 21(23), 7864; https://0-doi-org.brum.beds.ac.uk/10.3390/s21237864 - 26 Nov 2021
Cited by 8 | Viewed by 2466
Abstract
Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. [...] Read more.
Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders’ health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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18 pages, 908 KiB  
Article
Driver’s Personality and Behavior for Boosting Automobile Security and Sensing Health Problems Through Fuzzy Signal Detection Case Study: Mexico City
by Germán E. Baltazar Reyes, Pedro Ponce, Sergio Castellanos, José Alberto Galván Hernández, Uriel Sierra Cruz, Troy MacDaniel and Arturo Molina
Sensors 2021, 21(21), 7350; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217350 - 05 Nov 2021
Viewed by 1999
Abstract
Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user’s behavior was not explored in detail. Furthermore, efficiency gains could be had with [...] Read more.
Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user’s behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver’s personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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18 pages, 2061 KiB  
Article
Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study
by Swagata Das, Wataru Sakoda, Priyanka Ramasamy, Ramin Tadayon, Antonio Vega Ramirez and Yuichi Kurita
Sensors 2021, 21(19), 6459; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196459 - 27 Sep 2021
Cited by 3 | Viewed by 2732
Abstract
Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb [...] Read more.
Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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33 pages, 8126 KiB  
Article
A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction
by Tahjid Ashfaque Mostafa, Sara Soltaninejad, Tara L. McIsaac and Irene Cheng
Sensors 2021, 21(19), 6446; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196446 - 27 Sep 2021
Cited by 11 | Viewed by 2626
Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory [...] Read more.
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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20 pages, 2508 KiB  
Article
Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems
by Jorge D. Cardenas, Carlos A. Gutierrez and Ruth Aguilar-Ponce
Sensors 2021, 21(15), 5121; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155121 - 28 Jul 2021
Cited by 3 | Viewed by 2146
Abstract
The growing elderly population living independently demands remote systems for health monitoring. Falls are considered recurring fatal events and therefore have become a global health problem. Fall detection systems based on WiFi radio frequency signals still have limitations due to the difficulty of [...] Read more.
The growing elderly population living independently demands remote systems for health monitoring. Falls are considered recurring fatal events and therefore have become a global health problem. Fall detection systems based on WiFi radio frequency signals still have limitations due to the difficulty of differentiating the features of a fall from other similar activities. Additionally, the antenna orientation has not been taking into account as an influencing factor of classification performance. Therefore, we present in this paper an analysis of the classification performance in relation to the antenna orientation and the effects related to polarization and radiation pattern. Furthermore, the implementation of a device-free fall detection platform to collect empirical data on falls is shown. The platform measures the Doppler spectrum of a probe signal to extract the Doppler signatures generated by human movement and whose features can be used to identify falling events. The system explores two antenna polarization: horizontal and vertical. The accuracy reached by horizontal polarization is 92% with a false negative rate of 8%. Vertical polarization achieved 50% accuracy and false negatives rate. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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16 pages, 3459 KiB  
Article
Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring
by Nasim Hajari, Carlos Lastre-Dominguez, Chester Ho, Oscar Ibarra-Manzano and Irene Cheng
Sensors 2021, 21(13), 4356; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134356 - 25 Jun 2021
Cited by 3 | Viewed by 2039
Abstract
Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the [...] Read more.
Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient’s in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person’s pose, and locate and track high-risk regions over time so that necessary attention can be provided. Full article
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
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