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This article introduces a novel middleware that utilizes cost-effective, low-power computing devices like Raspberry Pi to analyze data from wireless sensor networks (WSNs). It is designed for indoor settings like historical buildings and museums, tracking visitors and identifying points of interest. It serves as an evacuation aid by monitoring occupancy and gauging the popularity of specific areas, subjects, or art exhibitions. The middleware employs a basic form of the MapReduce algorithm to gather WSN data and distribute it across available computer nodes. Data collected by RFID sensors on visitor badges is stored on mini-computers placed in exhibition rooms and then transmitted to a remote database after a preset time frame. Utilizing MapReduce for data analysis and a leader election algorithm for fault tolerance, this middleware showcases its viability through metrics, demonstrating applications like swift prototyping and accurate validation of findings. Despite using simpler hardware, its performance matches resource-intensive methods involving audiovisual and AI techniques. This design's innovation lies in its fault-tolerant, distributed setup using budget-friendly, low-power devices rather than resource-heavy hardware or methods. Successfully tested at a historical building in Greece (M. Hatzidakis' residence), it is tailored for indoor spaces. This paper compares its algorithmic application layer with other implementations, highlighting its technical strengths and advantages. Particularly relevant in the wake of the COVID-19 pandemic and general monitoring middleware for indoor locations, this middleware holds promise in tracking visitor counts and overall building occupancy.

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Variations in Global Positioning Systems (GPSs) have been used for tracking users' locations. However, when location tracking is needed for an indoor space, such as a house or building, then an alternative means of precise position tracking may be required because GPS signals can be severely attenuated or completely blocked. In our approach to indoor positioning, we developed an indoor localization system that minimizes the amount of effort and cost needed by the end user to put the system to use. This indoor localization system detects the user's room-level location within a house or indoor space in which the system has been installed. We combine the use of Bluetooth Low Energy beacons and a smartwatch Bluetooth scanner to determine which room the user is located in. Our system has been developed specifically to create a low-complexity localization system using the Nearest Neighbor algorithm and a moving average filter to improve results. We evaluated our system across a household under two different operating conditions: first, using three rooms in the house, and then using five rooms. The system was able to achieve an overall accuracy of 85.9% when testing in three rooms and 92.106% across five rooms. Accuracy also varied by region, with most of the regions performing above 96% accuracy, and most false-positive incidents occurring within transitory areas between regions. By reducing the amount of processing used by our approach, the end-user is able to use other applications and services on the smartwatch concurrently.

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Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

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The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation-in terms of either positioning or tracking-for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.

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BACKGROUND: Clinical studies in multiple sclerosis (MS) often require accurate measurement of walking distance. Utilisation of electronic devices could theoretically improve this. Mobile devices have the potential to continuously monitor health by collecting movement data. Popular fitness trackers record steps taken and distance travelled, typically using a fixed-stride length. However, applications using fixed-stride length may be less accurate in those with altered gait patterns. While useful for everyday purposes, medical monitoring requires greater accuracy. OBJECTIVE: Our aim was to determine the agreement and reliability of using a smartphone application to measure distance walked. METHOD: A phone application (mSteps) was developed and tested in a pilot study and then a validation study, looking at an indoor and outdoor setting with people with multiple sclerosis (PwMS) and a control cohort. RESULTS: In the pilot study, the 95% limits of agreement (LOA) for outdoor tracking in control cohort lay within the a priori defined limit; however, the indoor tracking in both cohorts did not meet the defined limit. The app was then successfully validated outdoors in PwMS. CONCLUSION: mSteps could be used to accurately measure distance outdoors in PwMS. There is still a need for solutions to accurately and reliably measure distance walked indoors.

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Features such as safety and longer flight times render lighter-than-air robots strong candidates for indoor navigation applications involving people. However, the existing interactive mobility solutions using such robots lack the capability to follow a long-distance user in a relatively larger indoor space. At the same time, the tracking data delivered to these robots are sensitive to uncertainties in indoor environments such as varying intensities of light and electromagnetic field disturbances. Regarding the above shortcomings, we proposed an ultra-wideband (UWB)-based lighter-than-air indoor robot for user-centered interactive applications. We developed the data processing scheme over a robot operating system (ROS) framework to accommodate the robot's integration needs for a user-centered interactive application. In order to explore the user interaction with the robot at a long-distance, the dual interactions (i.e., user footprint following and user intention recognition) were proposed by equipping the user with a hand-held UWB sensor. Finally, experiments were conducted inside a professional arena to validate the robot's pose tracking in which 3D positioning was compared with the 3D laser sensor, and to reveal the applicability of the user-centered autonomous following of the robot according to the dual interactions.

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A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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In futsal there are two referees on the playing court and their capacity to respond to physical and physiological demands imposed during the game is essential for the success. The futsal characteristics such as size pitch, referees position and rules of games or type of league could impose specific physical efforts probably. The aim of this study were to analyze the physical demands of eight elite referees (age 40 ± 3.43 years; height 1.80 ± 0.03 m; weight 72.84 ± 4.01 kg) from seven matches of Spanish Futsal Cup 2020. The physical activity of each referee during the match was monitored with a Local Positioning System, which was installed on futsal pitch where the matches were played. The data differences were evaluated as Paired-Samples T-Test procedure. The results revealed a similar total distance between halves (2888.39 vs. 2831.51 m). The zone 3 distance (15.1-18 km⋅h-1) showed a significative decrease (p < 0.05) during the match in comparison to the first and second halves (-24.48 m; CI95%: -9.54 to -39.42; ES: 0.56). The number of high-intensity acceleration (-10.29; CI95%; 3.71-16.86; ES: 0.92) and deceleration (-24.86; CI95%; 11.59-38.12; ES: 0.99) decreased in the second half of the match (p < 0.05). Therefore, the use of the tracking device to monitoring physical performance provides knowledge of the specific activity profile from futsal referees. This information to can be useful to design more accurate the training programs.

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In this paper, an overlapping-resistant Internet of Things (IoT) solution for a Bluetooth Low Energy (BLE)-based indoor tracking system (BLE-ITS) is presented. The BLE-ITS is a promising, inexpensive alternative to the well-known GPS. It can be used in human traffic analysis, such as indoor tourist facilities. Tourists or other customers are tagged by a unique MAC address assigned to a simple and energy-saving BLE beacon emitter. Their location is determined by a distributed and scalable network of popular Raspberry Pi microcomputers equipped with BLE and WiFi/Ethernet modules. Only simple triggered messages in the form of login records (LRs) are sent to a server, where the so-called path vectors (PVs) and interest profile (IPr) are set. The authors implemented the prototype and demonstrated its usefulness in a controlled environment. As it is shown in the paper, the solution is highly overlap-resistant and mitigates the so-called multilocation problem.

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The aim of this study was to analyze the influence of the match half and the playing position on physical requirements in the Spanish Professional Futsal League players during official games. The external load from distance, speed, acceleration and deceleration variables were obtained from fourteen elite futsal players during 10 official matches of the 2019-2020 season using a Local Positioning System with ultra-wideband technology installed on the futsal pitch. The results revealed similar results from physical requirements between first and second half (p > 0.05). Wingers demonstrated greater high-speed running distance (+4.04 m·min-1; CI95%: 0.35 to 7.72; ES: 0.87) than pivots (p > 0.05). There were a high number of accelerations (7.42-9.41 n·min-1) and decelerations (7.37-9.12 n·min-1) per minute in all player positions. The principal finding of the current manuscript did not evidence differences in the physical performance of players between the first and second half. The physical requirements varied among pivots and wingers regarding high-intensity actions. These outcomes add new contributions to the understanding of futsal physical demands.

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Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person's movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person's and beacons' localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant's localization error was 1.05 ± 0.44 m, and the average beacons' localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons' data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM).

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The aim of this study was to evaluate the validity and reliability of a commercially available local position measurement (LPM) tracking system when assessing distance and running time at different speeds. Fifteen male healthy athletes performed 15 m displacements at walking, running and sprinting speed. Data recorded by the LPM system were compared to those from the reference equipment, consisting of measuring tape and electronic timing gates placed at 0, 5, 10, and 15 m. Mean error, mean absolute error (MAE), standard deviation (SD) of the measurement error, maximum measurement error and root mean square error (RMSE) were calculated to determine the validity for distance and the running time variables. Product-moment correlation and intraclass correlation coefficient (ICC) were also used for the running time. Finally, the reliability of the distance was carried out comparing data from the three repetitions with the standard tape measure using a linear mixed model and the typical error as mean coefficient of variation (CV) (%). MAE shows errors under 0.18 m for the distance variable at all speeds and under 0.08 s for the running time variable at all speeds, except from 15 m at walking. Product-moment correlations were high to nearly perfect for running time (range: 0.60-0.99), ICC varied between high (0.75-0.90) and extremely high (>0.99) for most measures, and coefficients of variation remained almost invariable as speed increased (walking: 2.16; running: 2.52; sprinting: 2.20). The tested LPM system represents a valid and reliable method for monitoring distance during different constant speeds over a straight line, as long as there is no signal loss. However, the running time errors could be too large for performance tests that require acute precision.

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Wheelchair mobility performance is an important aspect in most wheelchair court sports, commonly measured with an indoor tracking system or wheelchair bound inertial sensors. Both methods provide key wheelchair mobility performance outcomes regarding speed. In this study, we compared speed profiles of both methods to gain insight into the level of agreement, for recommendations regarding future performance measurement. Data were obtained from 5 male highly trained wheelchair basketball players during match play. Players were equipped simultaneously with a tag on the footplate for the indoor tracking system (∼8 Hz) and inertial sensors on both wheels and frame (199.8 Hz). Being part of a larger study on 3 vs 3 player game formats, data were collected in several matches with varying field sizes, but activity profiles closely resembled regular match play. Both systems provide similar outcomes regarding distance covered and average speed. Due to differences in sampling frequency and sensor location (reference point) on the wheelchair (for speed calculation), minor differences were revealed at low speeds (<2.5 m/s). Since both systems provide complementary features, a hybrid solution as proved feasible in this study, could possibly serve as the new gold standard for mobility performance measurement in wheelchair basketball or wheelchair court sports in general.

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Estimating an occupant's location is arguably the most fundamental sensing task in smart buildings. The applications for fine-grained, responsive building operations require the location sensing systems to provide location estimates in real time, also known as indoor tracking. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphone to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel combines the noisy sensor readings with the floormap information to estimate locations. One key observation supporting our work is that occupants exhibit distinctive motion characteristics at different locations on the floormap, e.g., constrained motion along the corridor or in the cubicle zones, and free movement in the open space. While extensive research has been performed on using a floormap as a tool to obtain correct walking trajectories without wall-crossings, there have been few attempts to incorporate the knowledge of space use available from the floormap into the location estimation. This paper argues that the knowledge of space use as an additional information source presents new opportunities for indoor tracking. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework, and the Context-Augmented Particle Filtering algorithm is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve accuracy improvement of 31.3% compared with the purely WiFi-based tracking system.

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Navigating in unknown big indoor environments with static 2D maps is a challenge, especially when time is a critical factor. In order to provide a mobile assistant, capable of supporting people while navigating in indoor locations, an accurate and reliable localization system is required in almost every corner of the building. We present a solution to this problem through a hybrid tracking system specifically designed for complex indoor spaces, which runs on mobile devices like smartphones or tablets. The developed algorithm only uses the available sensors built into standard mobile devices, especially the inertial sensors and the RGB camera. The combination of multiple optical tracking technologies, such as 2D natural features and features of more complex three-dimensional structures guarantees the robustness of the system. All processing is done locally and no network connection is needed. State-of-the-art indoor tracking approaches use mainly radio-frequency signals like Wi-Fi or Bluetooth for localizing a user. In contrast to these approaches, the main advantage of the developed system is the capability of delivering a continuous 3D position and orientation of the mobile device with centimeter accuracy. This makes it usable for localization and 3D augmentation purposes, e.g. navigation tasks or location-based information visualization.

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We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a person's movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to perform tracking accurately. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors.

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BACKGROUND: When respiratory therapists (RTs) seek respiratory care equipment, finding it quickly is desirable, both to expedite patient care and to avert RTs wasting time. To optimize RTs' ability to quickly locate ventilators, we developed and implemented a radio-frequency identification (RFID) tagging system called eTrak. METHODS: The Clinical Engineering and Information Technology groups at Cleveland Clinic collaboratively developed a Wi-Fi-based RFID program that used active RFID tags. Altogether, 218 ventilators, 82 noninvasive ventilators, and various non-respiratory equipment were tagged, beginning in March 2010. We calculated the difference in time required to locate equipment before versus after implementation. RESULTS: The eTrak system had a mean 145 log-ons per week over the first year of use, and was associated with a decreased time required for RTs to locate ventilators: median 18 min (range 1-45 min) versus 3 min (range 1-6 min) (P < .001). Surveys of RTs regarding whether equipment was hard to find before versus after implementing eTrak showed a non-significant trend toward improvement. CONCLUSIONS: An RFID tracking system for respiratory equipment shortened the time to locate ventilators and non-significantly improved RT satisfaction with finding equipment. RFID tagging of equipment warrants further investigation.

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The population of elderly people is increasing rapidly in many developed nations. Providing safe and comfortable care to aging people is an important social goal. Moreover, obtaining correct activity and location information for an elderly person is an important research goal. This work proposes a novel intelligent RFID-based indoor tracking system for elderly people living alone. The proposed system uses environment information for inhabitants and received signal strength of an RFID reader to estimate the probable location of an inhabitant. The proposed system then coordinates with the wireless sensor node of a three-axis accelerometer and uses a genetic algorithm to compute the location of the inhabitant. The proposed system also uses context and gait information to improve inhabitant-tracking accuracy. Experiment results show that the accuracy of the proposed system is better than that of existing RFID-based systems.

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