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Sleep plays a vital role in good health and well-being throughout human life. A great deal of studies have been done to detect sleep stages. Most of the current sleep monitoring systems are invasive to users, e.g. requiring users to wear a device during sleep. In this paper, we use microphone to detect sleep stages including deep sleep, light sleep and rapid eye movement (REM), and propose a convolutional neural network using spectrogram as input. This paper's contribution mainly concentrates on the following two aspects: First, microphone is unobtrusive for sleep detection. Second, we build the mapping between acoustic signal and sleep stages with little manual intervention to extract features. Performance of the proposed method is validated on a realistic environmental dataset containing 52 nights of 5 participants. Experimental results show that the accuracy of sleep stages detection is superior to the representative off-the-shelf applications. Besides, we propose to utilize the attention maps to visualize acoustic data to better understand the relationship between sound and sleep stages. Experimental results show that the model has the ability to effectively reduce noises in classification by ignoring the high-frequency sounds and white noises.

Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. Indoor localization is an essential modules of these applications. A primary concern in designing scenario-tailored application is to obtain precise estimated distance combatting with harsh indoor wireless signal propagation issues, particularly multipath effect. The conventional propagation model based on received signal strength indicator(RSSI) is easily affected by temporal and spatial fluctuation due to the multipath effect, which leads to most of the distance estimation errors in current indoor localization systems. Intuitively, these positions in weak multipath effect(WME) conditions, which are slightly affected by multipath effect, perform better under free space propagation model. Therefore, the ability to distinguish weak multipath effect, which is slightly affected by multipath effect is a key enabler for accurate distance estimation. Enabling such capabilities on commercial WiFi infrastructure, however, is difficult due to the coarse multipath resolution with the MAC layer RSSI. In this paper, we propose a universal precise distance estimation scheme based on weak multipath effect identification, leveraging the channel state information(CSI) from the PHY layer. In our distance estimation system, we first select positions which are identified as weak multipath effect conditions. Then we build a free space propagation model with RSSI to estimate distance between the transmitter and the receiver, choosing these selected positions. Experimental results demonstrate that choosing positions in weak multipath effect conditions can effectively improve the accuracy of distance estimation in a variety of typical indoor environments.

Falls threaten the health and life of the elders heavily because they lead to injuries or even death. Therefore, a reliable monitoring and alarm mechanism is desperately in need to guarantee the quality of elders' life. In this paper, we propose a multistage machine learning method to perform fall detection and solve the false alarm and missing alarm problem in traditional fall detection methods. Our proposed method consists of three stages: 1) threshold filtering, 2) ELM classifier, and 3) orientation-based filtering. Our method utilizes a high-precision triaxial accelerometer to collect the relevant information. After filtered by our three-stage method, the signal can be determined whether it is a fall or not. Experimental results demonstrate that: different from the traditional state-of-art methods with a single machine learning classifier, our method can greatly reduce the missing alarm and false alarm rate on the premise of high accuracy for all detection.

Miscellaneous mini-wearable devices (e.g. wristbands, smartwatches, armbands) have emerged in our life, capable of recognizing activities of daily living, monitoring health information, so on. Conventional activity recognition (AR) models deployed inside these devices are generic classifiers learned offline from abundant data. Transferring generic model to user-oriented model requires time-consuming human effort for annotations. To solve this problem, we propose SS-ARTMAP-AR, a self-supervised incremental learning AR model updated from surrounding information such as Bluetooth, Wi-Fi, GPS, GSM data without user's annotation effort. Experimental results show that SS-ARTMAP-AR can gradually adapt individual users, become more incremental intelligence.

Accurate indoor localization is very important for various kinds of Location Based Services (LBS). For most of traditional approaches, the location estimation problems assume the availability of a vast amount of labeled calibrated data, which requires a great deal of manual effort. Previous researches cannot deal with this problem in both calibration reduction, location accuracy. In this paper, we propose a heterogeneous data driven manifold regularization model known as HeterMan to calibration-effort reduction for tracking a mobile node in a wireless sensor network. With the constraint of user heading orientation, we build a mapping function between signal space, physical space with extremely less labeled data, a large amount of unlabeled data. Experimental results show that we can achieve high accuracy with extremely less calibration effort comparing with previous methods. Furthermore, our method can reduce computation complexity, time consumption by parallel processing while maintaining high accuracy.

By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual behavior, the built classifier seldom deals with newly appeared contexts, which results in degrading the recognition performance greatly. To address this problem, we propose, an OSELM (online sequential extreme learning machine) based class incremental learning method for continuous and unobtrusive sensing new classes of social contexts from dynamic Bluetooth data alone. We integrate fuzzy clustering technique and OSELM to discover and recognize social contextual behaviors by real-world Bluetooth sensor data. Experimental results show that our method can automatically cope with incremental classes of social contexts that appear unpredictably in the real-world. Further, our proposed method have the effective recognition capability for both original known classes and newly appeared unknown classes, respectively.

Recent years, miscellaneous mini-wearable devices (e.g. wristbands, wristwatches, armbands) have emerged in our lives to recognize daily activities for the users. Owning to the limitations of hardware, Activity Recognition (AR) models running inside the device are bound to certain challenges, such as processing power, storage capability and battery life. This paper proposes an activity recognition system by considering three limitations above, and a model generation framework to construct AR models which are lightweight in different phases in model generation.

Recent years, a variety of infrastructure-mediated sensing methods have been proposed to recognize activities of daily living (ADLs). However, due to the inconvenience such as high-cost, difficult-to-install, intrusive and applicable to the house with specific architectures alone, existing water-use activity recognition methods cannot be widely used into people's houses. In this paper, a single-point infrastructure-mediated sensing technique is proposed for water-use activity recognition. A single 3-axis accelerometer sensor is attached to the surface of the main water pipe in the house to detect and collect the vibration signals of the main water pipe. These signal data are then processed through six modules in the proposed activity recognition system. Four classes of water-use activities (Bathing, Flushing toilet, Cooking and Washing) are classified by the system and experimental results show that our system can recognize about 70.37% water-use activities.

The regularization parameter and kernel parameter play important roles in the performance of the least squares support vector machine (LS-SVM). Aimed at optimizing the LS-SVM's parameters, a fast method based on distance is presented. The method is by way of calculating the various types of distances in the feature space to determine the optimal kernel parameter. Since the method only needs to calculate some simple mathematical formulas, and avoids training the corresponding LS-SVM classifiers, the method can greatly reduce the training time. Experiment results show that the proposed method can improve the training speed.