An indoor positioning system based on inertial sensors in smartphone

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.

Nowadays, stationary and locomotion activity recognition, also known as SLAR, is becoming increasingly important in a variety of domains, such as indoor localization, fitness activity tracking, and elderly care. Currently used methods typically involve handcrafted feature extraction, a process that is both difficult and requires specialized knowledge, and results can still be subpar. We proposed a deep learning technique for SLAR called LSTM-XGB that uses data from inertial sensors in smartphones to reduce the effort required for feature development and selection. The proposed LSTM-XGB consists of multiple stacked LSTM layers to automatically learn the temporal features of the input, followed by XGBoost for label prediction in the final layer. The results showed that the proposed LSTM-XGB technique, which automatically extracts features, outperforms conventional machine learning that requires manual feature extraction. We also showed that sensor data from three sensors (accelerometer, linear acceleration, and gyroscope) can be combined. This achieved higher accuracy than other combinations or single sensors.

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points.

This article studies the suitability of smartphones with built-in inertial sensors for biofeedback applications. Biofeedback systems use various sensors to measure body functions and parameters. These sensor data are analyzed, and the results are communicated back to the user, who then tries to act on the feedback signals. Smartphone inertial sensors can be used to capture body movements in biomechanical biofeedback systems. These sensors exhibit various inaccuracies that induce significant angular and positional errors. We studied deterministic and random errors of smartphone accelerometers and gyroscopes, primarily focusing on their biases. Based on extensive measurements, we determined accelerometer and gyroscope noise models and bias variation ranges. Then, we compiled a table of predicted positional and angular errors under various biofeedback system operation conditions. We suggest several bias compensation options that are suitable for various examples of use in real-time biofeedback applications. Measurements within the developed experimental biofeedback application show that under certain conditions, even uncompensated sensors can be used for real-time biofeedback. For general use, especially for more demanding biofeedback applications, sensor biases should be compensated. We are convinced that real-time biofeedback systems based on smartphone inertial sensors are applicable to many similar examples in sports, healthcare, and other areas.

Obtaining accurate floor plans of buildings is critical for optimising indoor geographic information system (GIS) applications. In this paper, we present a room measurement tool that utilises a smartphone equipped with an ultrasonic sensor. To take measurements, users complete a lap along the walls of all of the rooms. Then the tool accurately estimates the shape and size of them by tracking the walking paths of users and measuring the distance from the path to the walls with ultrasonic sensors. To track walking paths, we utilise inertial sensors embedded in the smartphone to estimate walking steps and turns, and the ultrasonic sensors to estimate the stride length when walking toward the wall. To account for such adjacent objects as bookshelves that decrease the accuracy of room size estimation, we used a mixed Gaussian filter. Our experimental results show that our tool considerably improved the estimation accuracy of the room shape and size.

Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window-based displacement scheme is designed to generate a time-series-based feature data set. We further apply the long short-term memory (LSTM) network for data fusion and localization on this data set by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

Smartphones are increasing ubiquitously due to the need and demand in the modern era. The world is transmuting into a global village with the cumulation of smart devices. Nowadays, smartphones are enriched with inertial sensors which can be used to recognize physical human activities in the wild. Human activity recognition (HAR) lies at the core of many applications like health monitoring, fall detection, road safety, personal assistance, and behavior-based context awareness. Context-based HAR is a new dimension that provides fine-grained information about the action being performed and leads us towards the automated and intelligent system design, which is useful to furnish the smart solution to real-life problems. In this paper, smartphone sensors are utilized to propose a framework for human activity and context recognition. This framework performs activity-dependent context recognition on the ExtraSensory dataset using random forest, decision tree, and k-nearest neighbor classifiers, and achieves an accuracy of 93.10%, demonstrating the effectiveness of the proposed framework.

To overcome the limitation of Global positioning system (GPS) in indoor environments, various indoor positioning system have been developed using Wi-Fi, Bluetooth, Ultrawideband (UWB) and radio-frequency identification (RFID). Amongst them, Wi-Fi technologies are most commonly used for indoor navigation. Wi-Fi signals may be unavailable in some areas due to obstacles and unreachable coverages. Despite of it, the accuracy achieved by Wi-Fi is between 5–15 m that is unfavorable for visually impaired people. The popularity of beacons for positioning and smartphones with built-in inertial sensors plays a vital role in developing potential indoor navigation system. This paper presents a framework for visually impaired person (VIP) based on inertial sensors of smartphones and Bluetooth beacons. Beacons/proximity sensors in a building can help a pedestrian to navigate between two landmarks/points of interest via turn-by-turn navigation. However, there are certain areas in the building where external sensing is absent in a big hallway or dark alley. This model demonstrates that inertial sensors are useful to track a VIP in dark areas. Also. minimizes the use of external sensors between two landmarks/beacons. The performance of the proposed framework with the fusion algorithm in an android application is examined by conducting trajectory test on a smartphone. The experimental results of the walking traces show that the system has high accuracy with almost 1.5-2 m mean position error which could be improved further by implementing magnetometer based position learning techniques.

In order to design a small size, cheap, space sensing, and wireless communication human machine interface (HMI), we developed a smartphone-based system. It uses the accelerometer and the gyroscope of the smartphone to generate six commands. The velocity and position have to be estimated using the measured acceleration. In this paper, offset cancellation and high-pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration signals in real-time applications. The integration accuracy is improved compared with other numerical integrators.

One of the most recent and popular tourism application is the virtual visits (virtual tour guide) which are based on Augmented/Virtual Reality technology (AR/VR). Such systems suffer from the lack of precise indoor geolocation of visitors within the cultural heritage site. This issue can be resolved by using smartphone (and tablets) embedded sensors, which can catch a huge number of data on visitor behaviour (acceleration, orientation, etc.). This data can be fused using dedicated methods (Extended Kalman Filter (EKF), Particle Filter, etc.) in order to estimate visitor position. However, such algorithms are highly dependent on: sampling time, sensors technologie, number of used sensors, etc. With this kind of solution the error in position estimation growth with the covered distance. In this paper, we present an hybrid solution for reducing error in time without additional infrastructure such Wifi, etc. For this, we present an indoor geolocation solution based on smartphone inertial sensors and earth magnetic field. The proposed method is divided into three phases: heading estimation using 3 sensors (accelerometer, compass and gyroscope) with an Extended Kalman Filter, steps detection and step length estimation, error correction using fingerprinting. The proposed solution is implemented on a smartphone (Samsung-Galaxy S7 based on Android OS) and maintain the error under of 1.5m.

Human Activity Recognition is a research field that aims to identify the activities carried out by a person. Recognition can be done by using information that is retrieved from various sources, for example: using inertial sensors. Lately, many people use smartphone with built-in inertial sensors such as accelerometer and gyroscope which make these smartphone capable of recognizing human activities. But, an optimization must be done to minimize the computational process of the recognition system so that the system could be function properly in smartphone that has limited processing power. In this study optimization was done by reducing the numbers of features used in the dataset. Using an available public human activity recognition dataset, mRMR (Minimum Redundancy Maximum Relevance) feature selection method was applied to the dataset to reduce the numbers of features. Results from this study show that using mRMR method in dataset 7∶3, the numbers of features can be reduced from 561 features to 201 features, while still maintaining the accuracy at 95.15% using SVM classifier and 94.23% using MLP classifier. While using mRMR method in dataset 8∶2, the numbers of features can be reduced from 561 features to 154 features with the accuracy 95.18% using SVM and the numbers of features can be reduced from 561 to 211 with the accuracy 94.84% using MLP. Both dataset using the same threshold that is 95% for SVM and 94% for MLP. For the running time computation, in dataset 7∶3 the running time becomes 42.57% from the initial running time using SVM and 12.63% from the initial running time using MLP. While in dataset 8∶2 the running time becomes 60.5% from the initial running time using SVM and 14% from the initial running time using MLP.

Abstract. Knowing the position has been an ambition in many areas such as science, military, business, etc. GPS was the realization of this wish in 1970s. Technological advances such as ubiquitous computing, as a conquering perspective, requires any service to work for any user, any place, anytime, and via any network. As GPS cannot provide services in indoor environments, many scientists began to develop indoor positioning systems (IPS). Smart phones penetrating our everyday lives were a great platform to host IPS applications. Sensors in smart phones were another big motive to develop IPS applications. Many researchers have been working on the topic developing various applications. However, the applications introduced lack simplicity. In other words, they need to install a step counter or smart phone on the ankle, which makes it awkward and inapplicable in many situations. In the current study, a new IPS methodology is introduced using only the usual embedded sensors in the smart phones. The robustness of this methodology cannot compete with those of the aforementioned approaches. The price paid for simplicity was decreasing robustness and complicating the methods and formulations. However, methods or tricks to harness the errors to an acceptable range are introduced as the future works.

The topic of indoor positioning and indoor navigation by using observations from smartphone sensors is very challenging as the determined trajectories can be subject to significant deviations compared to the route travelled in reality. Especially the calculation of the direction of movement is the critical part of pedestrian positioning approaches such as Pedestrian Dead Reckoning (“PDR”). Due to distinct systematic effects in filtered trajectories, it can be assumed that there are systematic deviations present in the observations from smartphone sensors. This article has two aims: one is to enable the estimation of partial redundancies for each observation as well as for observation groups. Partial redundancies are a measure for the reliability indicating how well systematic deviations can be detected in single observations used in PDR. The second aim is to analyze the behavior of partial redundancy by modifying the stochastic and functional model of the Kalman filter. The equations relating the observations to the orientation are condition equations, which do not exhibit the typical structure of the Gauss-Markov model (“GMM”), wherein the observations are linear and can be formulated as functions of the states. To calculate and analyze the partial redundancy of the observations from smartphone-sensors used in PDR, the system equation and the measurement equation of a Kalman filter as well as the redundancy matrix need to be derived in the Gauss-Helmert model (“GHM”). These derivations are introduced in this article and lead to a novel Kalman filter structure based on condition equations, enabling reliability assessment of each observation.

The outreach of Wi-Fi localization is extended in this study for urban wide applications as they provide the high potential to employ them for numerous applications for localization and guidance in urban environments. The selected application presented in this paper is the localization and routing of public transport smartphone users. For the conducted investigations, Received Signal Strength Indicator (RSSI) values are collected for users who are travelling from home in a residential neighbourhood to work in the city centre and return along the same route. Special tramway trains are selected which provide two on-board Wi-Fi Access Points (APs). Firstly, the availability, visibility and RSSI stability of the Wi-Fi signal behavior of these APs and the APs in the surrounding environment along the routes is analyzed. Then the trajectories are estimated based on location fingerprinting. A first analyses reveals that significant differences exists between the six employed smartphones as well as times of the day, e. g. in the morning at peak hours or at off-peak hours. From the long-time observations it is seen that the two on-board APs show a high stability of the RSSI signals at the same times of the day and along the whole route. It is therefore currently investigated how they can confirm and validate user localization along the route and if they can contribute to constrain the overall positioning solution in combination with the inertial smartphone sensors. Moreover, the railway track can serve as a further constraint. As an outlook on future work, the development of a Simultaneous Localization and Mapping (SLAM) solution with a fusion with the smartphone inertial sensors is proposed.

For decades, the identification of human action has been one of the most critical study areas in artificial intelligence research. The study in human activity recognition aims to classify people's behavior based on data that is already accessible, such as pictures, visual, sensing, or stream recorded data, among other things. A common distinction made by most human activity recognition methods is between daily tasks such as walking and specific behaviors like standing or sitting. More specialized functions targeted to the use-case of pedestrian interior navigation are investigated in this study. These activities include several types of stationary and movement activities and various other activities. A deep learning-based human activity recognition framework is presented that can be used to recognize people's actions inside an indoor localization situation by analyzing smartphone inertial sensor data. Furthermore, we introduced a 1D-ResNet-SE model in the framework and assessed it using a publicly available dataset known as the Activity Logs dataset. According to the experimental measurements, the 1D-ResNet-SE surpasses other standard deep learning techniques in terms of accuracy, with a maximum accuracy of 84.11% in the general scenario.