Zhenghua Chen

Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization

Location-based services (LBS) have attracted a great deal of attention recently. Outdoor localization can be solved by the GPS technique, but how to accurately and efficiently localize pedestrians in indoor environments is still a challenging problem. Recent techniques based on WiFi or pedestrian dead reckoning (PDR) have several limiting problems, such as the variation of WiFi signals and the drift of PDR. An auxiliary tool for indoor localization is landmarks, which can be easily identified based on specific sensor patterns in the environment, and this will be exploited in our proposed approach. In this work, we propose a sensor fusion framework for combining WiFi, PDR and landmarks. Since the whole system is running on a smartphone, which is resource limited, we formulate the sensor fusion problem in a linear perspective, then a Kalman filter is applied instead of a particle filter, which is widely used in the literature. Furthermore, novel techniques to enhance the accuracy of individual approaches are adopted. In the experiments, an Android app is developed for real-time indoor localization and navigation. A comparison has been made between our proposed approach and individual approaches. The results show significant improvement using our proposed framework. Our proposed system can provide an average localization accuracy of 1 m.

Smartphone Inertial Sensor-Based Indoor Localization and Tracking With iBeacon Corrections

The Global Positioning System (GPS) can be readily used for outdoor localization, but GPS signals are degraded in indoor environments. How to develop a robust and accurate indoor localization system is an emergent task. In this paper, we propose a smartphone inertial sensor-based indoor localization and tracking system with occasional iBeacon corrections. Some important issues in a smartphone-based pedestrian dead reckoning (PDR) approach, i.e., step detection, walking direction estimation, and initial point estimation, are studied. One problem of the PDR approach is the drift with walking distance. We apply a recent technology, iBeacon, to occasionally calibrate the drift of the PDR approach. By analyzing iBeacon measurements, we define an efficient calibration range where an extended Kalman filter is utilized. The proposed localization and tracking system can be implemented in resource-limited smartphones. To evaluate the performance of the proposed approach, real experiments under two different environments have been conducted. The experimental results demonstrated the effectiveness of the proposed approach. We also tested the localization accuracy with respect to the number of iBeacons.

Indoor localization using smartphone sensors and iBeacons

Outdoor localization can be readily obtained by Global Positioning System (GPS). Since GPS signals are denied in indoor environments, technology for accurate indoor localization is still an active on-going research topic. In this paper, we present a framework of combining smartphone sensors and iBeacons for accurate indoor localization. The Pedestrian Dead Reckoning (PDR) can be applied for localization using smartphone sensors. Unfortunately, it will drift with walking distance. Therefore, iBeacons are leveraged to correct the drift of the PDR approach. A particle filter is performed for the correction. Real experiments show a significant improvement of the localization accuracy with sparse iBeacon deployment in a 47.3m × 15.9m area. Moreover, since the estimation accuracy of the initial point is vital for the PDR approach, we evaluate the performance of our proposed approach with respect to the estimation error of the initial point. The results underline the robustness of our proposed approach for the initial point estimation error.

Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings

Occupancy information can help us to achieve high energy-efficient buildings. Previous works mainly focus on predicting the presence and absence of occupants in homes or single person offices. We attempt to predict regular occupancy level in a commercial building deployment scenario. The occupancy prediction models can be divided into two categories of occupancy models and data mining approaches. For the occupancy models, we shall investigate the efficiencies of two widely used multi-occupant models, that is, inhomogeneous Markov chain and multivariate Gaussian. For the data mining approaches, we propose the application of autoregressive integrated moving average, artificial neural network and support vector regression. Experiments have been conducted using actual occupancy data under four different prediction horizons, that is, 15 min, 30 min, 1 and 2 h. The results demonstrated a guideline in how to choose a proper method for the prediction of occupancy in commercial buildings under different prediction horizons.

Accurate indoor localization and tracking using mobile phone inertial sensors, WiFi and iBeacon

In this paper, we propose a robust and accurate indoor localization and tracking system using smartphone built-in inertial measurement unit (IMU) sensors, WiFi received signal strength measurements and opportunistic iBeacon corrections based on particle filter. We utilize Pedestrian Dead Reckoning (PDR) approach which leverages smartphone equipped accelerometers, gyroscope and magnetometer to estimate the walking distance and direction of user. The position estimated by WiFi fingerprinting based approach is fused with PDR to reduce its drifting error. Since the number of WiFi routers is usually limited for localization in large-scale indoor environment, we employ the emerging iBeacon technology to occasionally correct the drifting error of PDR in poor WiFi coverage area. Extensive experiments have been conducted and verified the superiority of the proposed system in terms of localization accuracy and robustness.

Environmental Sensors-Based Occupancy Estimation in Buildings via IHMM-MLR

Occupancy estimation in buildings can benefit various applications such as heating, ventilation, and air-conditioning control, space monitoring, and emergency evacuation. Due to the consideration of temporal dependency in occupancy data, hidden Markov model (HMM) has been shown to be effective in occupancy estimation. However, the conventional HMM that assumes invariant temporal dependency of occupancy dynamics for different time instances is unrealistic. Moreover, the performance of the conventional HMM that utilizes mixture of Gaussian for emission probability in terms of continuous observations can be easily affected by the noise in sensory data. To address these problems, in this paper, we propose a new architecture, i.e., inhomogeneous hidden Markov model with multinomial logistic regression (IHMM-MLR), for building occupancy estimation using nonintrusive environmental sensors. Instead of using the time-invariant transition probability matrix, we apply a time-dependent (inhomogeneous) transition probability matrix which can capture the temporal dependency for different time instances. Meanwhile, we employ an efficient probabilistic model, i.e., MLR, for emission probability. Online and offline occupancy estimation schemes are presented for real-time and accurate long-term applications respectively. Real experiments have indicated the effectiveness of our proposed approach.

Smartphone-based Human Activity Recognition in buildings using Locality-constrained Linear Coding

Smartphone-based Human Activity Recognition (HAR) is an active research topic in smart homes and health care. Smartphones are often carried by users in a non-intrusive way, and the sensing platform in them could provide much information about the users. By leveraging the built-in accelerometer and gyroscope in smartphones, we can design a system to distinguish the users simple behaviors. Most of the current work did not consider feature selection, but directly fed the statistical features from both the time and frequency domains into machine learning algorithms. In this work, we proposed an approach for HAR by using Locality-constrained Linear Coding (LLC) as a feature selector to improve the performance of HAR systems. After feature selection, standard typical classifiers of Support Vector Machine, K-Nearest-Neighbor, Kernel-Extreme Learning Machine and Sparse Representation Classifier can then be applied to learn the distinctiveness of the selected features. Experiment results showed that the LLC approach achieved an average accuracy of about 90% due to a better selected dictionary for feature representation. Activities of simple actions that occupants usually perform in buildings include walking, walking upstairs and downstairs, running and static. This work on occupant behaviors could be used in energy-efficient building and health care applications.

Robust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM

Human activity recognition using either wearable devices or smartphones can benefit various applications including healthcare, fitness, smart home, etc. Instead of using wearable devices which are intrusive and require extra cost, we shall leverage on modern smartphones embedded with a variety of sensors. Due to the flexibility of using smartphones, the recognition accuracy will degrade with orientation, placement, and subject variations. In this paper, we propose a robust human activity recognition system in terms of orientation, placement, and subject variations based on coordinate transformation and principal component analysis (CT-PCA) and online support vector machine (OSVM). The proposed CT-PCA scheme is utilized to eliminate the effect of orientation variations. Experiments show that the proposed scheme significantly improves the activity recognition accuracy and outperforms the state-of-the-art methods on leave one orientation out experiments, which demonstrates the generalization ability of the proposed scheme on the data from unseen orientations. We also show the effectiveness of this scheme on placement and subject variations. However, the inherent difference of signal properties for different placement and subject dramatically reduces the recognition accuracy, especially for different placement. Thus, we present an efficient OSVM algorithm, that is, online-independent support vector machine (OISVM), which utilizes a small portion of data from the unseen placement or subject to online update the parameters of the SVM algorithm. The experimental results demonstrate the effectiveness of this OISVM algorithm on placement and subject variations.

Building Occupancy Estimation with Environmental Sensors via CDBLSTM

Buildings consume quite a lot of energy; hence, the issue of building energy efficiency has attracted a great deal of attention in recent years. A key factor in achieving this objective is occupancy information that directly impacts on energy-related building control systems. In this paper, we leverage on environmental sensors that are nonintrusive and cost-effective for building occupancy estimation. Our result relies on feature engineering and learning. The conventional feature engineering requires one to manually extract relevant features without a clear guideline. This blind feature extraction is labor intensive and may miss some significant implicit features. To address this issue, we propose a convolutional deep bidirectional long short-term memory (CDBLSTM) approach that contains a convolutional network and a deep structure to automatically learn significant features from the sensory data without human intervention. Moreover, the long short-term memory networks are able to capture temporal dependencies in the data and the bidirectional structure can take the past and future contexts into consideration for the final identification of occupancy. We have conducted real experiments to evaluate the performance of our proposed CDBLSTM approach. Instead of estimating the exact number of occupants, we attempt to identify the range of occupants, i.e., zero, low, medium, and high, which is adequate for most of building control systems. The experimental results indicate the effectiveness of our proposed approach compared with the state-of-the-art methods.

Modeling building occupancy using a novel inhomogeneous Markov chain approach

Accurate modeling of building occupancy is an important issue in building energy optimization, but it is a difficult problem due to its stochastic property. This paper proposed an inhomogeneous Markov chain approach to model building occupancy under two scenarios of multi-occupant single-zone (MOSZ) and multi-occupant multi-zone (MOMZ). In this study, we define the state of the Markov chain as an increment of the number of the occupants, which is quite different from the traditional method using the number of the occupants as the state. Simulations have been done to verify the performance of the proposed method, and mean occupancy profile and four random variables such as time of first arrival are evaluated to assess the advantages of the proposed approach. In the MOSZ scenario, the proposed model performs very well and significantly outperforms the existing agent-based model using the actual measurement data from a real building. The proposed model can also adequately capture the trend of the occupancy dynamics in the MOMZ scenario.