The Timed Up and Go (TUG) test is widely used to assess fall risk by evaluating walking speed and reaction time. Dividing the TUG test into distinct phases improves the accuracy of fall risk assessment and helps identify specific movement difficulties. Various approaches have been proposed to automatically measure the duration of each phase, with external camera systems being the most common. However, these systems often require optimal camera placement and an unobstructed field of view, limiting their practicality in real-world settings. To address these limitations, we propose TUGTrack, a system that uses a wearable camera to capture first-person perspective images for TUG analysis. The system first applies the Farneback optical flow algorithm to extract motion features, and then uses a machine learning model to automatically segment the test into its different phases. An empirical study involving sixty healthy young adult participants who performed either normal or simulated elderly gait showed that TUGTrack achieves a phase identification accuracy of 98.68%, with a mean segmentation error of less than 0.11 seconds. A usability evaluation further confirmed the system’s practicality and ease of use, demonstrating its potential for movement analysis and clinical applications.

Relieving stress is crucial for the health of modern individuals. Deep breathing exercises have been shown to be effective for stress relief. However, the effectiveness of deep breathing exercises is limited if feedback on breathing patterns is not provided instantly. For this, we propose a wearable device called Anti-Stress, which can provide users with breathing patterns in real-time, enabling them to dynamically adjust their breathing according to Anti-Stress instructions for optimal relaxation. In addition, Anti-Stress provides users with objective stress indices by measuring the user's heart rate variability (HRV). The accuracy of Anti-Stress in detecting breathing patterns is up to 99%, and its response time is less than 200 ms, ensuring users can receive immediate guidance. To evaluate the effectiveness of Anti-Stress, we recruited 60 participants to conduct an empirical experiment. The ANCOVA analysis shows that the experimental group significantly reduces stress by using Anti-Stress compared to the control group without it. Furthermore, the usability questionnaires including SUS and QUIS demonstrate Anti-Stress's higher usability compared to a traditional non-biofeedback app. Our analysis also reveals that people with high openness or neuroticism personalities had better stress relief by using Anti-Stress. The results of this study demonstrate the feasibility and practicality of our respiratory feedback mechanism in helping users relieve stress and enhancing the effectiveness of deep breathing exercises.

Breathing exercises have gained popularity as a stress management technique. They provide benefits such as improved lung function, reduced stress, and better quality of life. However, the repetitive nature of these exercises can become boring over time, decreasing people’s motivation to continue practicing them. In this work, we developed BreathRelax, a breath-based video game that integrates a temperature sensor to detect respiratory signals and provides real-time feedback through computer gaming. BreathRelax can achieve an accuracy rate of 95% in recognizing breathing patterns, with a response time of 0.23 s, demonstrating its reliable and responsive performance. To evaluate the effectiveness of BreathRelax, 60 participants were invited for a comprehensive experiment. ANCOVA analysis demonstrated BreathRelax’s superior stress reduction through significantly improved RMSSD, SDNN, and pNN50 stress indices. In addition, the usability evaluation revealed that participants experienced high enjoyment and engagement in the game while finding it easy to operate. BreathRelax delivered promising results in both relaxation effects and user experience.

Stress and anxiety have become prevalent issues in today's society, exacerbated by the challenges of modern life. Deep breathing is an effective technique for reducing stress and anxiety. However, without proper feedback, its effectiveness may be limited, especially for beginners. To address this challenge, we designed an interactive smart face mask, named S-Mask, which can recognize a user's breathing patterns and provide real-time feedback during the breathing exercises. S- Mask hardware includes an embedded board equipped with a temperature sensor to detect and process respiration signals. Also, the real-time operating system FreeRTOS is adopted to coordinate multiple tasks running on the embedded board, such as sensor reading, breathing pattern recognition, and Bluetooth communication. An interactive mobile app is developed to guide users to complete the breathing exercises. In our experiment, 30 volunteers were invited to evaluate the responsiveness, accuracy, and usability of S-Mask. Our results show that S-Mask can achieve a 99.7% accuracy rate of breath recognition and has a response time of less than 200ms. The user interface is easy to use and can help users complete the breathing exercises efficiently.

The COVID-19 pandemic has already ravaged the world for two years and infected more than 600 million people, having an irreparable impact on the health, economic, and political dimensions of human society. There have been many papers proposing various Internet of Things (IoT) solutions to stop the spread of COVID-19, such as virus tracking, infection control, medical treatment, and health management. Due to the high popularity of IoT devices, many studies have systematically investigated related technologies, evaluating the development, and application of IoT solutions from different aspects, such as electronic security, personal privacy, disaster prediction, and artificial intelligence (AI). However, the usefulness of IoT in combating COVID-19 has not been thoroughly evaluated. Therefore, this article systematically analyzes high-quality articles published in well-known international journals and conferences from 2019 to 2022 and divides them into the technical development of prevention and treatment according to the application field of the solution, cross-comparing the research results. It provides a comprehensive assessment of the maturity of the current IoT solutions through the Standard Technology Readiness Level (TRL). Finally, through the technical efficacy matrix, the application status and development direction of the international IoT technology are analyzed by patent. Our results reveal the maturity and limitations of existing technologies to fight COVID-19. Possible future research directions are also discussed.

The smoothness of human-smartphone interaction directly influences users experience and affects their purchase decisions. A commonly used method to improve user interaction of smartphones is to optimize the CPU scheduler. However, optimizing the CPU scheduler requires a modification of operating system. In addition, the improvement of the smoothness of human-smartphone interaction may be limited because the display subsystem is not optimized. Therefore, in this paper, we design a motion prediction queuing system, named MPQS, to improve the smoothness of human-smartphone interaction. For this, we use the information of vector, speed, movement, provided by the queuing mechanism of Android, to predict the movement of user-smartphone interaction. Based on the prediction, we then utilize available execution time between frames to perform image processing. We conducted a set of experiments on beagleboard-xM to evaluate the performance of MPQS. Our experiment results show that the proposed method can reduce the number of jank by up to 21.75%.

This paper describes the structure, design and implementation of a building/environment data and information system, called BeDIS for short. It is designed to support fine-scale, location-specific services provided by smart devices and mobile applications in large smart buildings. Structured as a fog, it remains responsive when overloaded and degrades gracefully when network connection is disrupted and parts of it damaged. During normal times, it enables hundreds and thousands of people to locate themselves sufficiently accurately and navigate amidst dense crowd and moving objects via their mobile phones. When triggered by a disaster/emergency alert from responsible government agencies or the building safety system, BeDIS functions as a system of micro data servers for delivering location- and situation-specific emergency response instructions to people and decision support data to active smart devices and applications within fractions of a second to seconds.

The subsystem in a smartphone means its hardware components, such as the CPU, GPU, and screen. Accurately estimating subsystem power consumption of commercial smartphones is necessary for applicable to wide research areas. Current subsystem power estimation techniques are mostly based on power models, resulting in considerable errors for various types of power consumption behaviors. These include (1) asynchrony between the measured power consumption and the corresponding workload statistics, and (2) nonlinearity concerning CPU idle states, pixels colors of AMOLED screen, and GPU workload statistics. In this study, we propose a novel utilization-based, subsystem power estimation method for a smartphone, namely Clustering and Symbolic Regression (CSR) that takes these power consumption behaviors into account so as to increase power estimation accuracy. To address asynchrony, we cluster the subsystem workload statistics into synchronous and asynchronous groups by employing affinity propagation clustering. To address nonlinearity, we employ symbolic regression for fitting measured power consumptions with respect to subsystem workload statistics. We compare our approach with various power estimation methods, Linear Regression Model (LM), Genetic Programming (GP), and Support Vector Regression (SVR). The results show Mean Absolute Percentage Error (MAPE) reduction between 23.61 and 42.55 percent on the estimated power consumption of a simple (Nexus S) and complex (Galaxy S4) smartphone subsystems.

The building/environment data and information system (BeDIS) described here is a part of infrastructure needed to support location-specific, active emergency preparedness and responses within large buildings. Building/environment data and information (BeDI) based indoor positioning system (BeDIPS) is one of its components. BeDIPS can provide people in large buildings with sufficiently accurate location data. It is scalable, disaster resilient, and easy to configure, deploy, and maintain. BeDIPS works without Internet and serves both smart phones and most legacy Bluetooth devices. The other component of BeDIS is BeDI mist, a virtual repository of data and information on the building, interior layouts, and facilities. The mist uses micro data servers and smart gateways to deliver fine-scale, location-specific decision support data on a timely basis to hundreds and thousands of active devices and mobile applications.

Using the data obtained from a battery monitoring unit (BMU) are a low-cost, easy-to-use way to estimate the power consumption of smartphones. Current online power estimation methods produce significant errors compared to using external power monitors because online methods do not address the three most important factors which affect the efficacy of online power consumption estimates. These are: (1) battery capacity degradation with aging, (2) asynchronous power consumption, and (3) the effect of state-of-charge (SoC) difference, the remaining power level of the battery. This paper presents a semi-online power estimate method which uses charging data to determine actual battery capacity, applies the discrepancy between battery voltage at different workloads for asynchronous power detection, and analyzes the range of SoC (0-100 percent) which causes minimal power estimate errors. The proposed method is validated by conducting a series of experiments on a smartphone and comparing the results with the existing online power estimation methods. Experimental results on the power consumption estimates of real applications indicate that the semi-online method reduced the error rate of power estimates obtained from existing online methods by 27-94 percent. Moreover, this work also shows that battery capacity degradation is the major factor affecting the efficacy of online power estimations.

Automated GUI testing consists of simulating user events and validating the changes in the GUI in order to determine if an Android application meets specifications. Traditional record-replay testing tools mainly focus on facilitating the test case writing process but not the replay and verification process. The accuracy of testing tools degrades significantly when the device under test (DUT) is under heavy load. In order to improve the accuracy, our previous work, SPAG, uses event batching and smart wait function to eliminate the uncertainty of the replay process and adopts GUI layout information to verify the testing results. SPAG maintains an accuracy of up to 99.5 percent and outperforms existing methods. In this work, we propose smart phone automated GUI testing tool with camera (SPAG-C), an extension of SPAG, to test an Android hardware device. Our goal is to further reduce the time required to record test cases and increase reusability of the test oracle without compromising test accuracy. In the record stage, SPAG captures screenshots from device's frame buffer and writes verification commands into the test case. Unlike SPAG, SPAG-C captures the screenshots from an external camera instead of frame buffer. In the replay stage, SPAG-C automatically performs image comparison while SPAG simply performs a string comparison to verify the test results. In order to make SPAG-C reusable for different devices and to allow bettersynchronization at the time of capturing images, we develop a new architecture that uses an external camera and Web services to decouple the test oracle. Our experiments show that recording a test case using SPAG-C's automatic verification is as fast as SPAG's but more accurate. Moreover, SPAG-C is 50 to 75 percent faster than SPAG in achieving the same test accuracy. With reusability, SPAG-C reduces the testing time from days to hours for heterogeneous devices.

Many disaster warning and response systems can improve their surveillance coverage of the threatened area by supplementing in situ and remote physical sensor data with crowdsourced human sensor data captured and sent by people in the area. This paper presents fusion methods which enable a crowdsourcing enhanced system to use human sensor data and physical sensor data synergistically to improve its sensor coverage and the quality of its decisions. The methods are built on results of classical statistical detection and estimation theory and use value fusion and decision fusion of human sensor data and physical sensor data in a coherent way. They are the building blocks of a central fusion unit in a crowdsourcing support system for disaster surveillance and early warning applications.

This paper presents Robotic Application Resource Management Services (RARMS), a collection of tools for integrating reusable software components of a wide class of robotic applications on Microsoft Windows, a general-purpose, commodity operating system. RARMS includes (1) a resource allocation tool called RAAPT-HV for partitioning the available processors into a specified number of virtual processors, allocating available resources to virtual processors and assigning robotic software components to virtual processors and (2) a robot-class scheduling service (RC SS) which helps independently developed components prioritize relative to each other in a way that is consistent with their timing requirements. These tools aim to provide time-sensitive components with satisfactory responsiveness in an open environment without serious impact on the performance of other components. To demonstrate the effectiveness of RARMS, we adopted for experimentation and evaluation purposes several commonly-used components of delivery robots, including face detection, speech recognition, video streaming and path planning. The results of our experiments showed that these tools can help to achieve satisfactory performance for these software components of robotic applications.

The quality of an environmental monitoring application can often be measured by the number of samples collected per unit time. This is to guarantee the reconstruction of the monitored signal. For applications that use wireless sensors for untethered monitoring at rural areas, the challenge is to satisfy the sensing quality while maintaining a long-running operation. Due to the spatial locality of the monitored signal and the oftentimes dense deployment of sensors, the samples taken by a set of nearby sensors can be regarded as equivalent. The problem is how to assign the sampling workload to this set of sensors and maintain a long-running operation. This requires taking account of the energy required to sample as well as to transmit and relay the data to the sink. Previous works on energy-aware routing are not applicable due to the added complexity of sampling assignment. In this paper, we formulate this problem as an optimization problem and present the optimal solution. We compare our algorithm with two commonly used sampling workload assignment algorithms for balancing energy consumption through PowerTOSSIM. The results show that our algorithm can achieve as much as a 25 percent improvement in the residual energy of the critical node, which is the node with the lowest residual energy among all nodes.

A heterogeneous multiprocessor (HeMP) system consists of several heterogeneous processors, each of which is specially designed to deliver the best energy-saving performance for a particular category of applications. A low-power real-time scheduling algorithm is required to schedule tasks on such a system to minimize its energy consumption and complete all tasks by their deadlines. Existing works assume that processor speeds are known asa prioriand cannot deliver the optimal energy-saving performance. The problem of determining the optimal voltage for each processor to minimize the total energy consumption is called a voltage-setup problem. To the best of our knowledge, this is the first paper to propose the optimal solution for the HeMP single-level voltage-setup problem. This paper provides an optimal solution for the HeMP single-level voltage-setup problem. We first formulate the problem as a nonlinear generalized assignment problem that has been proved to be nondeterministic polynomial-time hard (NP-hard). We next develop a pruning-based algorithm to obtain the optimal solution. A heuristic algorithm is also proposed to derive an approximate solution. After obtaining the optimal partition, each processor's speed is determined by its final workload. In our simulations, we model more than a couple dozens of off-the-shelf embedded processors including ARM processor and TI DSP. The results show that the pruning-based algorithm reduces the time needed to derive the optimal solution by at least 98%, compared with the exhaustive search. Also, our heuristic algorithm achieves the minimum energy consumption over existing works.

A multimedia server requires a real-time disk-scheduling algorithm to deliver isochronous data for real-time streams. Traditional disk-scheduling algorithms focus on providing good quality in a best effort manner. In this paper, we propose a novel real-time disk-scheduling algorithm called WRR-SCAN (weighted-round-robin-SCAN) to provide quality guarantees for all in-service streams encoded at variable bit rates and bounded response times for aperiodic jobs. WRR-SCAN divides a real-time stream into guaranteed jobs and optional jobs. The admission control admits a stream as long as its guaranteed jobs are satisfied. Such a decision is made in 0(1) time as WRR-SCAN reserves a fixed weight for each stream. WRR-SCAN incorporates an aggressive policy to dynamically reclaim unused bandwidth during runtime. The reclaimed bandwidth is used to serve optional jobs or more aperiodic jobs. We conducted a set of simulations to compare WRR-SCAN with a set of referred disk-scheduling algorithms. The evaluations are conducted on a commonly used disk simulator with traces from a real multimedia server. The experimental results show that WRR-SCAN provides significantly better quality for real-time streams and yields considerably shorter response times for aperiodic jobs.

A heterogeneous multi-processor (HeMP) system consists of several heterogeneous processors, each of which is specially designed to deliver the best energy-saving performance for a particular category of applications. A low-power real-time scheduling algorithm is required to schedule tasks on such a system to minimize its energy consumption and complete all tasks by their deadline. The problem of determining the optimal speed for each processor to minimize the total energy consumption is called the voltage setup problem. This paper provides a near-optimal solution for the HeMP single-level voltage setup problem. To our best knowledge, we are the first work that addresses this problem. Initially, each task is assigned to a processor in a local-optimal manner. We next propose a couple of solutions to reduce energy by migrating tasks between processors. Finally, we determine each processor's speed by its final workload and the deadline. We conducted a series of simulations to evaluate our algorithms. The results show that the local-optimal partition leads to a considerably better energy-saving schedule than a commonly-used homogeneous multi-processor scheduling algorithm. Furthermore, at all measurable configurations, our energy consumption is at most 3% more than the optimal value obtained by an exhaustive iteration of all possible task-to-processor assignments. In summary, our work is shown to provide a near-optimal solution at its polynomial-time complexity.

Traditional real-time disk-scheduling algorithms service real-time tasks according to their deadlines. Such a priority-based algorithm, although satisfying real-time constraints, yields low disk utilization due to the excessive disk-seek time. Furthermore, it results in prolonged response time or even starvation for aperiodic tasks. In this paper, we propose a novel rate-based real-time disk-scheduling algorithm called WRR-SCAN (Weighted-Round-Robin SCAN). WRR-SCAN guarantees to meet the deadline of a real-time task by reserving disk bandwidth according to its real-time constraints. WRR-SCAN services scheduled tasks in scan order to minimize the disk-seek time. In addition, WRR-SCAN delivers better response time for aperiodic tasks which are served in best-effort manner by priority based algorithms. We conducted a set of extensive experiments to compare WRR-SCAN and SCAN-EDF, a priority-based algorithm studied extensively in literature. The experimental results show that WRR-SCAN reduces non-transmission overhead significantly and produces a guaranteed minimum data rate for aperiodic tasks while keeping the deadlines of real-time tasks.