Smart Mobile Devices Research Articles

Graph Structure Learning on User Mobility Data for Social Relationship Inference

With the prevalence of smart mobile devices and location-based services, uncovering social relationships from human mobility data is of great value in real-world spatio-temporal applications ranging from friend recommendation, advertisement targeting to transportation scheduling. While a handful of sophisticated graph embedding techniques are developed for social relationship inference, they are significantly limited to the sparse and noisy nature of user mobility data, as they all ignore the essential problem of the existence of a large amount of noisy data unrelated to social activities in such mobility data. In this work, we present Social Relationship Inference Network (SRINet), a novel Graph Neural Network (GNN) framework, to improve inference performance by learning to remove noisy data. Specifically, we first construct a multiplex user meeting graph to model the spatial-temporal interactions among users in different semantic contexts. Our proposed SRINet tactfully combines the representation learning ability of Graph Convolutional Networks (GCNs) with the power of removing noisy edges of graph structure learning, which can learn effective user embeddings on the multiplex user meeting graph in a semi-supervised manner. Extensive experiments on three real-world datasets demonstrate the superiority of SRINet against state-of-the-art techniques in inferring social relationships from user mobility data. The source code of our method is available at https://github.com/qinguangming1999/SRINet.

The effect of handedness on mental arithmetic: A longitudinal large-scale investigation through smart mobile devices.

The effect of handedness on mental arithmetic: A longitudinal large-scale investigation through smart mobile devices.

Accuracy of Point-of-Care Blood Glucometers in Neonates and Critically Ill Adults

Inpatient glycemic management has become a common issue because of the increasing number of hospitalized patients with hyperglycemia. Point-of-care devices can enable timely inpatient glucose monitoring, which may lead to better outcomes. The accuracy of point-of-care testing in various clinical scenarios has been questioned, particularly in neonates and critically ill patients. This study aimed to evaluate the accuracy of the CONTOUR PLUS and CONTOUR PLUS ONE glucometers (new wireless systems that link to a smart mobile device) when used as point-of-care devices for blood glucose monitoring in neonates and critically ill adults in inpatient settings. This cross-sectional study was conducted at a medical center in central Taiwan and enrolled patients admitted to the neonatal intensive care unit, sick child room, or respiratory intensive care unit between November 2020 and April 2021. Neonates with suspected infection or abnormal blood coagulation and adults who had abnormal blood coagulation, were pregnant, had received organ transplants, or had undergone massive blood transfusions were excluded. The accuracy of the glucometers was determined based on the following criteria of the International Organization for Standardization (ISO) standard: 15197:2013. Overall, 114 neonates (mean age, 4.2 days [range, 0-28 days]; 65 boys [57.0%]) and 106 hospitalized critically ill adults (mean age, 68.2 years [range, 27-94 years]; 72 men [67.9%]) were enrolled in this study. The glucose values obtained with each glucometer had good precision, and all findings met the reference criteria of the within-lot results. All measurements of the neonates' venous blood by each glucometer met the accuracy criteria specified by ISO standard 15197:2013. Furthermore, 98.1% and 97.2% of the arterial blood glucose measurements for critically ill adults obtained with CONTOUR PLUS and CONTOUR PLUS ONE met the accuracy criteria, respectively. Both glucose management systems met the accuracy criteria for venous blood from neonates and arterial blood from critically ill adults. Thus, the use of these 2 point-of-care devices in inpatient settings, including for neonates and critically ill adults, can be recommended to minimize limitations associated with the clinical application of point-of-care testing in glucose management. The wireless connection may play a role in the subsequent development of institution-wide virtual glycemic management under the supervision of a team of endocrinologists.

Performance Improvement of Speech Emotion Recognition Systems by Combining 1D CNN and LSTM with Data Augmentation

In recent years, the increasing popularity of smart mobile devices has made the interaction between devices and users, particularly through voice interaction, more crucial. By enabling smart devices to better understand users’ emotional states through voice data, it becomes possible to provide more personalized services. This paper proposes a novel machine learning model for speech emotion recognition called CLDNN, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN). To design a system that closely resembles the human auditory system in recognizing audio signals, this article uses the Mel-frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal are extracted as the input of the model. Local feature learning blocks (LFLBs) composed of one-dimensional CNNs are employed to calculate the feature values of the data. As audio signals are time-series data, the resulting feature values from LFLBs are then fed into the LSTM layer to enhance learning on the time-series level. Finally, fully connected layers are used for classification and prediction. The experimental evaluation of the proposed model utilizes three databases: RAVDESS, EMO-DB, and IEMOCAP. The results demonstrate that the LSTM model effectively models the features extracted from the 1D CNN due to the time-series characteristics of speech signals. Additionally, the data augmentation method applied in this paper proves beneficial in improving the recognition accuracy and stability of the systems for different databases. Furthermore, according to the experimental results, the proposed system achieves superior recognition rates compared to related research in speech emotion recognition.

Energy-Efficient and Variability-Resilient 11T SRAM Design Using Data-Aware Read-Write Assist (DARWA) Technique for Low-Power Applications.

The need for power-efficient devices, such as smart sensor nodes, mobile devices, and portable digital gadgets, is markedly increasing and these devices are becoming commonly used in daily life. These devices continue to demand an energy-efficient cache memory designed on Static Random-Access Memory (SRAM) with enhanced speed, performance, and stability to perform on-chip data processing and faster computations. This paper presents an energy-efficient and variability-resilient 11T (E2VR11T) SRAM cell, which is designed with a novel Data-Aware Read-Write Assist (DARWA) technique. The E2VR11T cell comprises 11 transistors and operates with single-ended read and dynamic differential write circuits. The simulated results in a 45 nm CMOS technology exhibit 71.63% and 58.77% lower read energy than ST9T and LP10T and lower write energies of 28.25% and 51.79% against S8T and LP10T cells, respectively. The leakage power is reduced by 56.32% and 40.90% compared to ST9T and LP10T cells. The read static noise margin (RSNM) is improved by 1.94× and 0.18×, while the write noise margin (WNM) is improved by 19.57% and 8.70% against C6T and S8T cells. The variability investigation using the Monte Carlo simulation on 5000 samples highly validates the robustness and variability resilience of the proposed cell. The improved overall performance of the proposed E2VR11T cell makes it suitable for low-power applications.

INTEGRATION OF TERRESTRIAL LASER SCANNING AND SMARTPHONE LIDAR: THE CASE STUDY OF LIDZBARK CASTLE

Abstract. In many cases, archaeological research allows to explain the history and evolution of heritage buildings. To support these studies, the availability of reliable 3D representations of such buildings has already been shown to be of remarkable importance. Thanks to the development of Terrestrial Laser Scanning (TLS) and close-range photogrammetric techniques, the generation of such 3D representations by means of remote sensing techniques has become quite common nowadays. While TLS can still be considered as the state-of-the-art method for generating accurate building geometric models, acquisition methods based on the use of mobile devices is becoming a quite attractive solution in order to speed up the survey while also enabling an easier data acquisition on certain narrow places. Despite a plethora of mobile mapping tools is currently available on the market, smartphones are clearly the most widely used smart mobile device in the world, being an ideal solution in terms of usability, portability, and spread. Motivated by this observation and by the recent availability of smartphones provided with Light Detection and Ranging (LiDAR) sensors, this paper aims at investigating the effectiveness of the integration of traditional TLS with smartphone LiDAR on the survey of the castle in Lidzbark Warminski, which dates back to the mid-fourteenth century. TLS, with the scanner set on a standard tripod, was used to scan easily accessible places. TLS mounted on a dedicated tripod, patented by the co-authors of the project was used for less easy-to-reach places. Finally, smartphone LiDAR enabled 3D data acquisition on very hard-to-reach locations, e.g. excavation areas. The model obtained by integrating the data acquired by all the considered methods allowed to properly describe all the area of interest of the castle, being usable for the technical documentation of such historic building. The obtained model significantly advanced the state of knowledge about the castle in Lidzbark Warminski. As a result of the work, many traces of the history of the complex were discovered, proving its gradual evolution.

Broadcast Attention Learning for Real Telephone Speech Keyword Spotting

With the development of mobile smart devices, keyword spotting plays an important role in the interaction between machines and users. However, low storage and low energy consumption of mobile devices limit the accuracies of keyword spotting tasks. Therefore, how to achieve a balance between the high accuracy and low consumption is a research hotspot for a keyword spotting system. Convolutional neural networks have been widely adopted in recent keyword spotting systems due to their superior accuracies, and the success of the transformer architecture in many areas demonstrates the effectiveness of self-attention. In this paper, we combine self-attention and convolutional neural networks, and propose the broadcast attention learning network (BA-net), using a small number of parameters while achieving the accuracies of 97.18% and 78.44% respectively on the Google speech command dataset and a real telephone speech dataset.

Task recommendation method for fusion of multi-view social relationship learning and reasoning in the mobile crowd sensing system

With the powerful sensing ability of mobile smart devices, sensing users can obtain crowd sensing services for intelligent devices in the Internet of Things. However, due to the low accuracy of task recommendations, the uneven quality of data provided by sensing users has always been a problem. In this paper, a novel mobile crowd sensing task recommendation framework is proposed by integrating multi-view social relationship reasoning, which aims to construct a social relation network through multi-view to ensure task recommendation accuracy and improve data quality. Firstly, the social relationship network is jointly built by location matching view, time series view, and preference view. Secondly, the sensing user preferences and candidate task representations are learned by extracting task name features, task subject features, and task category features. Finally, the key nodes are used to divide the social relationship sub-network. The preference representation of the key nodes and the candidate task representation is used to do the inner product to obtain the probability of the social relationship sub-network selection task, and then complete the task recommendation. Evaluations based on two real datasets, Gowalla and Brightkite, show that the average recommendation accuracy is about 93%, and the participation rate of sensing users is about 97%. At the same time, the running time is reduced by 15% on average compared with the baseline algorithm, and the mobile cost of sensing users is reduced.

Investigation of sleep quality and musculoskeletal pain of university students during the pandemic period

Background/Aim: The transition to distance education due to the coronavirus-19 restrictions changed the routines of university students, and physical activity and sleep status were affected due to increased computer screen use. Determining the factors affecting the sleep quality and musculoskeletal pain of university students during the pandemic period will guide the measures that can be taken to address these changes. The aim of this study was to investigate sleep quality and musculoskeletal pain of university students during the pandemic. Our research asked, “What are the factors affecting sleep quality during the pandemic period and does a difference between the musculoskeletal pain of those with good and bad sleep quality exist?”
 Methods: University students receiving distance education were included in this cross-sectional study. Students’ demographic characteristics, exercise habits, daily use of smart mobile devices were questioned via an online form. The Pittsburgh Sleep Quality Index was used to evaluate sleep quality, and the Cornell Musculoskeletal Discomfort Questionnaire was used to evaluate musculoskeletal pain. Sleep quality status based on regular exercise and daily mobile device usage time were compared. In addition, the musculoskeletal discomforts of the participants with good or bad sleep quality were compared.
 Results: Two-hundred twenty-one university students were included in the study (187 female, 34 male). Sleep quality was better in those who exercised regularly (P=0.005). Subjective sleep quality and sleep latency scores were better for those who used smart mobile devices less than 5 h a day (P=0.002 and P=0.018, respectively). Those with good sleep quality had less musculoskeletal discomfort (P<0.001).
 Conclusion: The findings of our study showed that increased physical inactivity and smart mobile device use during the distance education period negatively affect sleep quality. Since those with poor sleep quality have more musculoskeletal pain, we think that preventive measures, such as reducing screen time and inactivity, should be taken in distance education students to prevent health problems that may be associated with poor sleep status.

Segment based power-efficient scheduling for real-time DAG tasks on edge devices

Smart Mobile Devices (SMDs) are crucial for the edge computing paradigm’s real-world sensing. Real-time applications, which are computationally intensive and periodic with strict time constraints, can typically be used to replicate real-world sensing. Such applications call for increased processing speed, memory capacity, and battery life on SMDs, which are typically resource-constrained due to physical size restrictions. As a result, scheduling real-time applications for SMDs that are power efficient is crucial for the regular operation of edge computing platforms, and downstream decision-making tasks like computation offloading require the prediction of power consumption using power-saving approaches like DVFS. The main question is how to swiftly develop a better solution to the NP-Hard power efficient scheduling problem with DVFS. Thus, by segmenting the aligned tasks on an SMD, we present a segment-based analysis approach. Additionally, we offer a segment-based scheduling algorithm (SEDF) that draws inspiration from the segment-based analysis approach to achieve power-efficient scheduling for these real-time workloads. This segment-based approach yields a power consumption bound (PB), and a computation offloading use case is developed to demonstrate the application of PB in the subsequent decision-making processes. Both simulations and actual device tests are used to confirm the PB, SEDF, and the effectiveness of offloading decision-making. We demonstrate empirically that PB can be utilized to make approximative optimal decisions in decision-making problems involving computation offloading. SEDF is a straightforward and effective scheduling approach that can cut the power consumption of a multi-core SMD by roughly 30%.

Mobile applications identification using autoencoder based electromagnetic side channel analysis

Various applications are deployed on mobile smart devices in almost every situations of our life, while in some of these situations sensitive applications are strictly prohibited, such as cameras in cinemas and browsers in examination halls. Real-time recognition of applications running on mobile smart devices is of great significance in these cases. However, most of the existing technologies have the limitation that they require system permissions to obtain the running application list which is banned by mainstream mobile operating systems. Noting that the launch of a certain application will emit a unique pattern of magnetic field, we introduce magnetic field side channel analysis to recognize running applications. However, magnetic field side channel analysis is challenging since it is hard to extract features from magnetic field data without domain experts. Besides, real-time applications identification demands accurate detection of applications launching. To overcome these challenges, we extract robust depth features using autoencoder and implement online application recognition by introducing finite-state machine to identify the application launch window from raw data. The proposed method is evaluated by recognizing 1000 different applications in real environment. The experiment results show that the proposed method is feasible and effective in real-time application identification.

Android malware detection using PMCC heatmap and Fuzzy Unordered Rule Induction Algorithm (FURIA)

Many smart mobile devices, including smartphones, smart televisions, smart watches, and smart vacuums, have been powered by Android devices. Therefore, mobile devices have become the prime target for malware attacks due to their rapid development and utilization. Many security practitioners have adopted different approaches to detect malware. However, its attacks continuously evolve and spread, and the number of attacks is still increasing. Hence, it is important to detect Android malware since it could expose a great threat to the users. However, in machine learning intelligence detection, too many insignificant features will decrease the percentage of the detection’s accuracy. Therefore, there is a need to discover the significant features in a minimal amount to assist with machine learning detection. Consequently, this study proposes the Pearson correlation coefficient (PMCC), a coefficient that measures the linear relationship between all features. Afterwards, this study adopts the heatmap method to visualize the PMCC value in the color of the heat version. For machine learning classification algorithms, we used a type of fuzzy logic called lattice reasoning. This experiment used real 3799 Android samples with 217 features and achieved the best accuracy rate of detection of more than 98% by using Unordered Fuzzy Rule Induction (FURIA).

Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC

AbstractIn recent years, mobile edge computing (MEC) has become a promising solution to solve the shortage of technical resources in wireless body area networks (WBANs). However, the existing research work has not fully utilized the communication and computing resources in WBANs scenarios. To solve this problem, a task offloading framework that combined with cellular, WiFi networks and device‐to‐device communications is proposed, that makes full use of resources to improve system reliability. Considering that a single MEC server may be overloaded by a large number of patients, the total task offloading cost and load variance is formulated into a multi‐objective optimization problem (MOOP). A non‐dominated sorting genetic algorithm with smart mobile device ‐ patient connection matrix (NSGA ‐SPCM) to solve the MOOP. In view that an SDM may connect multiple patients at the same time during chromosome crossing, the SPCM can quickly detect the unfeasible gene location and mutate it into viable. Simulation results show that the proposed framework and algorithm have good performance.

An automated guide to COVID-19 and future pandemic prevention and management

In this paper, we present CoFighter, a mobile application for prevention and management of COVID-19 and other related pandemics in the globalized world. We took advantage of the proliferation of mobile smart devices in every home to design and implement an Android application for COVID-19 and similar pandemics. Since the outbreak of COVID-19 pandemic in 2019, there has been even more serious pressures on governments and health institutions on the best way to provide appropriate and reliable guide to individuals on how to contain the virus and similar pandemics in the future. Citizens have not been adequately informed of the various provisions and guides by their governments and the wide usage of social media had led to the spread of fake news, misinformation and conspiracy theories. It therefore becomes very necessary to develop a dynamic information repository in the form of a mobile application to help combat the spread of any pandemic whenever the need arises. The application provides information on COVID-19, vaccine challenges, prevention guides and cases management and timely updates to keep citizens properly and adequately informed. It makes provision for future similar pandemics that could throw the world into chaos as the CORONA virus did in 2019. The weaknesses and challenges observed in most popularly existing COVID-19 applications were highlighted and implemented in CoFighter. CoFighter provides users, governments and health workers with a platform not only to manage COVID-19 and other similar pandemics in the future, but also helps frontline health workers to better manage the pandemics. The developed application runs on an Android device with Android version 4.2 or higher and can be used not only to manage COVID-19 pandemic, but also to manage economic crisis and similar future pandemics. CoFighter is available via the Repository: https://github.com/OkeyIsOkay/CoFighter-Project.

Development of Three-Dimensional Breast Scan and Measurement Application Using Laser Imaging Detection and Ranging Sensor on iPhone.

Laser imaging detection and ranging (LiDAR) is a modern three-dimensional (3D) technology that uses a time-of-flight method based on the round-trip time of an infrared laser beam to detect the presence and features of objects. The iPhone 12 Pro is the first smart mobile device with built-in LiDAR sensors. The authors' team developed a software application based on iOS devices with built-in LiDAR sensors for 3D breast scanning and automatically analyzing the breast's geometric measurement. Breast geometry, including midclavicle-to-nipple distance, sternal notch-to-nipple distance, nipple-to-inframammary fold (IMF) distance, distance between nipples, and body circumference on nipple and IMF level were measured using the software application and tapeline. The relative technical error of measurement (rTEM) value was used to calculate the error ratios between the measurements acquired by the software application and those of the tapeline. Good rTEM values ranging from 2.99% to 5.19% were found in the midclavicle-to-nipple distance, sternal notch-to-nipple distance, distance between nipples, nipple-level circumference, and IMF-level circumference. However, there was a poor rTEM value greater than 10% in the nipple-to-IMF distance. The proposed software application using current iOS devices with built-in LiDAR sensors can provide an ideal 3D scanning system that has a low cost burden, good accuracy, portability, and ease of use.

Research on the Difficulties and Countermeasures Faced by Classroom Teaching Management in Higher Vocational Colleges in China

Higher vocational education is an important part of China's higher education, and its main goal is to train compound technical and technical talents who serve the needs of economic and social development and face the front line of economic and social development and production service. According to the empirical research, this study reveals the current difficulties of classroom teaching management in higher vocational colleges, which mainly include: low level of students and weak learning foundation; Students are not interested in their majors and are not optimistic about their employment prospects; The dependence on smart mobile devices is high, and the classroom order is difficult to standardize; Teachers' professional skills are insufficient, and the teaching content emphasizes theory over practice; School teaching management is imperfect. According to the current difficulties of classroom teaching management in higher vocational colleges, we need to take corresponding countermeasures and suggestions, mainly including: cultivating students' good study habits; Improve students' sense of professional belonging and enhance their learning motivation; Improve classroom teaching management skills and strengthen students' sense of discipline; Carefully prepare teaching contents and diversify teaching methods; Establish a "double-qualified" teacher team to improve teachers' professional skills; Further improve the school classroom teaching management system. Through the mutual restriction and promotion of teachers, students and schools, the maximum teaching force is finally formed, the classroom teaching management level of teachers is improved, the learning ability of students is improved, and the classroom teaching quality of higher vocational colleges is finally improved.

A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments

Mobile Edge Computing (MEC) has arisen as a promising computing paradigm consisting of three tiers: Smart Mobile Devices (SMDs), fog nodes, and the cloud. The MEC enables computational offloading and execution schedules to cope with the problems of insufficient resources for the SMDs and the computational tasks' deadlines. The offloading problem determines in what order and source of the network the tasks should be performed to minimize execution time and power consumption. The main aim of the current paper is to reduce execution time and energy consumption by optimizing tasks' offloading and scheduling in MEC networks. As a result, the task scheduling and offloading are modeled as an optimization problem. Then, an enhanced hybridization of Artificial Ecosystem-based Optimization (AEO) and Arithmetic Optimization Algorithm (AOA), named E-AEO-AOA, is presented to optimize it. In the E-AEO-AOA, the AOA and AEO algorithms are initially discretized. Next, the Q-learning strategy is modified and recruited to hybridize the algorithms in a complementary manner. Subsequently, chaos theory is utilized in a local search procedure to enhance the exploitation capability of the E-AEO-AOA. Eventually, the performance of E-AEO-AOA is examined on fifteen MEC networks. In the experiments, the E-AEO-AOA is compared with AEO, AO, AOA, JS, MRFO, STOA, SCA, and TSA algorithms statistically. Besides, the algorithms' convergence rate and solutions dispersity are visually compared. Moreover, the algorithms are compared by the Wilcoxon signed-rank test. The experimental results indicate that the E-AEO-AOA surpassed competitor algorithms in 90% of cases. Likewise, in 6% of the cases, the E-AEO-AOA produced the same results as AEO, AOA and MRFO.

Multi-UAV Placement and User Association in Uplink MIMO Ultra-Dense Wireless Networks

This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to the UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. The main goal of the paper is to jointly maximize the number of served terrestrial users and their scheduling. Moreover, the number of UAV servers and their 3D placement are optimized. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. The resulting problem is non-convex and intractable to solve. Therefore, we break the problem into two subproblems. We propose an efficient algorithm based on machine learning to solve the first subproblem, i.e., optimizing the number of UAVs and their 3D placements, and the user association. Different from existing literature, our proposed algorithm can achieve low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula> -norm concept to find a convex upper bound for the subproblem and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until the overall algorithm converges and a near-optimal solution is achieved for the optimization problem. Moreover, the computational complexity of the proposed scheme is analyzed. Finally, we evaluate the performance of our proposed algorithm via the simulation results. Regarding fast convergence and low computational complexity of the proposed algorithm, its superior performance is confirmed through numerical results.

A systematic literature review on mHealth related research during the COVID-19 outbreak

PurposeUsing smart mobile devices, called mobile health (mHealth), facilitates providing health services, speeds up the process and reduces the costs and complications of direct services. Also, mHealth has many capabilities and applications in epidemic and pandemic outbreaks. This study aimed to systematically review the mHealth adoption researches in epidemic/pandemic outbreaks and provide some suggestions for future research for tackling for COVID-19.Design/methodology/approachThe results produced in this study are based on the literature analysis of 36 articles on mHealth adoption. To find the relevant studies; searches were done in PubMed, Google, Web of Science and Scopus by related keywords during 2020–2022. After selecting the studies based on the inclusion and exclusion criteria, data were collected by using PRIZMA methods for systematically reviewing the literature.FindingsOf the 727 retrieved studies, 36 studies related to mHealth services during the pandemic situation were included. This has been performed by collecting data including demographic details, methodological details, limitations and significance of relationships between the constructs from the available articles based on the mHealth services. All studies emphasized the positive effect of mHealth for usage in epidemic/pandemic outbreaks.Research limitations/implicationsThe main applications of mHealth for epidemic/pandemic outbreaks included public health aspects, data management, educational programs, diagnosis as well as treatment. mHealth is an appropriate method for encountering epidemic/pandemic outbreaks due to its extensive applications. In the pandemic outbreak of COVID-19, mHealth is one of the best choices to use in the patient-physician relationship as Tele-visits, using in fever coach, providing real-time information for healthcare providers, population monitoring and detecting the diseases based on data obtained from different locations. These findings will help the mHealth providers to design their services accordingly.Originality/valueThis study contributes to the researchers and academicians by providing relevant information regarding the mHealth during the COVID-19 pandemic. This is the first time initiative to explore the research questions and future research direction for the researchers during the COVID-19 outbreak. Based on this, we present a comprehensive and actionable research agenda and practical implications.

Subtask Segmentation Methods of the Timed Up and Go Test and L Test Using Inertial Measurement Units—A Scoping Review

The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information on mobility status. To identify and compare current methods for subtask segmentation using inertial sensor data, a scoping review of the literature was conducted using PubMed, Scopus, and Google Scholar. Articles were identified that described subtask segmentation methods for the TUG and L Test using only inertial sensor data. The filtering method, ground truth estimation device, demographic, and algorithm type were compared. One article segmenting the L Test and 24 articles segmenting the TUG met the criteria. The articles were published between 2008 and 2022. Five studies used a mobile smart device’s inertial measurement system, while 20 studies used a varying number of external inertial measurement units. Healthy adults, people with Parkinson’s Disease, and the elderly were the most common demographics. A universally accepted method for segmenting the TUG test and the L Test has yet to be published. Angular velocity in the vertical and mediolateral directions were common signals for subtask differentiation. Increasing sample sizes and furthering the comparison of segmentation methods with the same test sets will allow us to expand the knowledge generated from these clinically accessible tests.