Passive indoor localization are critical for smart home intelligence services. Conventional methods using vision, acoustics, or radar face limitations in scalability and effectiveness due to their invasive nature. WiFi-based methods are emerging as a promising alternative because of the ubiquity of WiFi, its cost-efficiency, and non-intrusive manner. However, the performance of existing WiFi-based approaches in residential multi-room environments is often hampered by the limited bandwidth of standard WiFi devices. In this work, we introduce an innovative system that leverages commodity WiFi for precise human presence detection and room-level localization. Our system applies a novel multipath selection technique to focus a limited set of multipaths relevant to proximate motions to the device. We also introduce a spatial feature that capitalizes on multiple antennas, further improving spatial resolution and detection accuracy. Our method robustly integrates time, frequency, and spatial domain features for reliable room identification for human presence. The evaluations, considering real-world residential house and various walking patterns, validate the system’s robustness and high performance, achieving 87.62% test accuracy, an 88.03% test true positive rate, and an 88.74% test positive predictive value, surpassing state-of-the-art methods by over 25 %, showing its effectiveness in the real world.

Device-free indoor object detection and localization are essential for the success of smart homes. Traditional vision/acoustic/radar-based approaches face operational constraints that limit their effectiveness and scalability. WiFi-based approaches have recently been a promising candidate due to their ubiquity, cost-effectiveness, and privacy-preserving nature. However, most of them show inadequate performance in typical residential settings due to the limited WiFi bandwidth and the resulting low spatial resolution. In this article, we introduce a novel system using commodity WiFi that can accurately determine the specific room where the person is, i.e., room-level localization. The system employs a novel multipath selection technique to concentrate on a limited set of multipaths predominated by the proximate motions to the device. Based on the technique, a spatial feature leveraging multiple antennas to enhance the spatial resolution is proposed for more refined detection coverage. Combining the spatial feature with time- and frequency-domain features, the system is shown to achieve an overall test accuracy of 87.63%, a true positive rate of 89.47%, and a positive predictive value of 88.51%, outperforming state-of-the-art methods by >20% and showing its potential for real-world applications.

Indoor falls have proved fatal to many people due to a lack of timely assistance. Existing approaches for fall detection using cameras and wearable devices intrude on privacy and cause inconvenience. Passive sensing approaches using radar have limited coverage and demand dense deployment. Current solutions using commercial off-the-shelf (COTS) WiFi devices are either environment-dependent or lack extensive testing in real environments to confidently assess false alarm rates. In this work, we propose a fusion approach to detect falls with COTS WiFi, where we leverage signal processing techniques to extract environment-independent features, and use a neural network to detect differentiating patterns in those features. We designed a lightweight Long Short-Term Memory (LSTM)-based neural network with only 21 k parameters that can easily be deployed on edge devices. We further provide a framework to explain the network's behavior that supports a calibration-free design. Our proposed FallAware system's detection performance has been extensively tested on $\sim$ 2400 falls gathered from over 25 volunteers in 5 different environments. In addition, we conducted long-term false alarm testing in 6 diverse environments for a total duration of 21 months. The results show that FallAware can detect falls with an average detection rate of 94.1% in unseen environments with $< $ 5 false alarms per month in single-person occupancy homes.

Indoor intelligent perception systems have gained significant attention in recent years. However, accurately detecting human presence can be challenging in the presence of non-human subjects such as pets, robots, and electrical appliances, limiting the practicality of these systems for widespread use. In this paper, we propose a novel system (“WI-MOID") that passively and unobtrusively distinguishes moving human and various non-human subjects using a single pair of commodity WiFi transceivers, without requiring any device on the subjects or restricting their movements. WI-MOID leverages a novel statistical electromagnetic wave theory-based multipath model to detect moving subjects, extracts physically and statistically explainable features of their motion, and accurately differentiates human and various non-human movements through walls, even in complex environments. In addition, WI-MOID is suitable for edge devices, requiring minimal computing resources and storage, and is environment-independent, making it easy to deploy in new environments with minimum effort. We evaluate the performance of WI-MOID in five distinct buildings with various moving subjects, including pets, vacuum robots, humans, and fans, and the results demonstrate that it achieves 97.34% accuracy and 1.75% false alarm rate for identification of human and non-human motion, and 95.98% accuracy in unseen environments without model tuning, demonstrating its robustness for ubiquitous use.

Addressing the pivotal challenge of discerning human and non-human activities in smart environments, in this demo, we present a system utilizing commercial WiFi transceivers for precise human and non-human motion differentiation through the walls. This system effectively filters non-human interference in smart home systems by extracting physically and statistically explainable features from ubiquitous WiFi signals. It passively recognizes moving subjects in real time without constraining their movement, even in complex environments. Tailored for edge computing, it ensures minimal resource consumption and generalizes well across various settings. Our long-term field tests confirm a high accuracy rate of 97.34% and a low false alarm rate of 1.75%, underscoring its robustness and readiness for practical deployment. Please find the companion video with the URL: https://youtu.be/6xkJZ_VvL9Q.

As WiFi becomes increasingly pervasive in communications, its role in sensing applications is likewise expanding. However, current WiFi-based sensing technologies often operate under the limit assumption that all detected motion originates from human activities, there by neglecting influences from non-human subjects. Being able to differentiate human motions from non-human ones is essential in many application use cases. This paper presents a deep learning framework that can accurately recognize human and various non-human moving subjects using single-pair WiFi devices, even through the walls. Utilizing environment-invariant features, the framework is tested across three settings with commodity WiFi devices and various deep neural network architectures for four-class recognition. Achieving an average validation accuracy of 95.57% and an average testing accuracy of 87.09% in unseen environments with a challenging dataset, our approach demonstrates its robustness and readiness for integration into intelligent IoT systems and applications.

As WiFi has become a ubiquitous medium for communication, its role in sensing applications has expanded. However, the current WiFi sensing applications are limited by their assumption that any detected motion signifies human activity, overlooking the potential impact of non-human subjects. Existing attempts to recognize the interference from non-human motion impose stringent requirements regarding device positioning, data quality, environmental complexity, and non-human subject categories. In this study, we design a robust deep learning framework, SrcSense (“ S ou rc e Sense ”), to recognize the motion source with WiFi signals through the wall. SrcSense extracts environment-independent features from single-link WiFi. We investigate the performance of popular deep neural networks and explore the efficacy of transferring pre-trained models to WiFi sensing tasks. We implement SrcSense and evaluate the performance in five real-world complex environments with commodity WiFi devices. With a challenging dataset considering large pets, diverse human activities and multiple subjects coexisting cases, SrcSense achieves an average validation accuracy of 95.84% across five distinct environments and an average testing accuracy of 91.71% in unseen environments without further model training or parameter tuning. By accumulating 20 seconds of WiFi data, SrcSense can achieve an elevated recognition accuracy of 99.77% with ResNet-50. These results underline the robustness of our approach and its readiness for integration into ubiquitous intelligent IoT systems and applications.

Indoor-location-based services rely on indoor maps, which are yet widely available despite numerous efforts from the industry. Existing solutions employ costly hardware (e.g., lidar) to achieve accurate mapping of indoor environments, or resort to crowdsourcing for floor plan generation at the cost of precision due to inaccurate inertial sensing. In this article, we leverage a new opportunity enabled by recent advances in RF-based inertial tracking that achieves centimeter accuracy. We present EZMAP, a high-accuracy, low-cost floor plan construction system that fuses RF and inertial sensing. EZMAP combines the fine-grained yet local information from RF tracking with the coarse grained but global contexts from inertial sensing (e.g., magnetic field strength), which together makes for an accurate map. Our system employs a robot for trajectory collection and requires only a single access point to be arbitrarily installed in the space, both of which are widely available nowadays. Furthermore, it can generate a map even only a small amount of data is available, allowing it to scale for different buildings, such as malls, office buildings, and homes with little cost. We validate the performance using a Dji RoboMaster S1 robot with commodity WiFi in three different buildings. The results show that our system can efficiently generate faithful maps for the targeted areas. With the ubiquity of the WiFi infrastructure and the rise of home robots, we believe our approach will pave the way for pervasive indoor maps services.

Proximity detection in indoor environments based on WiFi signals has gained significant attention in recent years. Existing works rely on the dynamic signal reflections and their extracted features are dependent on motion strength. To address this issue, we design a robust WiFi-based proximity detector by considering gait monitoring. Specifically, we propose a gait score that accurately evaluates gait presence by leveraging the speed estimated from the autocorrelation function (ACF) of channel state information (CSI). By combining this gait score with a proximity feature, our approach effectively distinguishes different transition patterns, enabling more reliable proximity detection. In addition, to enhance the stability of the detection process, we employ a state machine and extract temporal information, ensuring continuous proximity detection even during subtle movements. Extensive experiments conducted in different environments demonstrate an overall detection rate of 92.5 % and a low false alarm rate of 1.12% with a delay of 0.825s.

Indoor maps are essential to indoor location-based services, but are not widely accessible despite considerable efforts from the industry. Existing solutions employ costly hardware to achieve accurate mapping, or resort to laborious crowdsourcing methods, which may suffer from low accuracy due to inaccurate inertial sensing. In this paper, we leverage advanced RF-based inertial tracking and present a high-accuracy and low-cost floor plan reconstruction system. The proposed system combines local information from RF tracking with the global contexts from inertial sensing (e.g., magnetic field strength) for an accurate map. We validate the performance with commodity WiFi in an office building, which shows that the proposed system can efficiently generate faithful maps for a targeted area. With the ubiquitous deployment of WiFi devices, our approach will make a wide range of indoor location-based systems possible.

Recently, an extensive amount of research has focused on indoor intelligent perception applications and systems. However, the performance of these applications can be greatly impacted by the movement of non-human subjects, such as pets, robots, and electrical appliances, making them impractical for mass use. In this paper, we present the first system that passively and unobtrusively distinguishes between moving human and non-human subjects by a single pair of commodity WiFi transceivers, without requiring the subjects to wear any device or move in a restricted area. Our system can detect the moving subjects, extract physically and statistically explainable features of their motion, and distinguish non-human and human movements accordingly. Leveraging the state-of-the-art rich-scattering multi-path model, our system can differentiate human and non-human motion through the wall, even in complex environments. Built on environment-independent features, our system can be applied to new environments without further effort from users. We validate the performance with commodity WiFi in four different buildings on subjects including the pet, vacuum robot, human, and fan. The results show that our system achieves 97.7% recognition accuracy and a 95.7% true positive rate for non-human motion recognition. Furthermore, it achieves 95.2% accuracy for unseen environments without model tuning, demonstrating its accuracy and robustness for ubiquitous use.

'Fuji' apples produced in four counties of Shaanxi Province, China were used as samples to explore potential applications of portable spectrometers in determining sugar content and firmness of apples produced in different areas. Eighteen and twenty wavelengths were selected as characteristic wavelengths (CWs) using successive projections algorithm (SPA) from pretreated full spectra for sugar content and firmness, respectively. Two linear models (multiple linear regression (MLR) and partial least squares regression (PLSR)) and other two nonlinear models (general regression neural network (GRNN) and extreme learning machine (ELM) were adopted to build sugar content and firmness determination models. The results indicate that not only for sugar content, but also for firmness, PLSR had better performance than MLR, and ELM performed better than GRNN. PLSR-SPA had the best determination performance for sugar content and firmness. The research offers useful spectrometer technologies on developing portable detectors for internal qualities of apples.

Counting colonies is usually used in microbiological analysis to assess if samples meet microbiological criteria. Although manual counting remains gold standard, the process is subjective, tedious, and time-consuming. Some developed automatic counting methods could save labors and time, but their results are easily affected by uneven illumination and reflection of visible light. To offer a method which counts colonies automatically and is robust to light, we constructed a convenient and cost-effective system to obtain images of colonies at near-infrared light, and proposed an automatic method to detect and count colonies by processing images. The colonies cultured by using raw cows' milk were used as identification objects. The developed system mainly consisted of a visible/near-infrared camera and a circular near-infrared illuminator. The automatic method proposed to count colonies includes four steps, i.e., eliminating noises outside agar plate, removing plate rim and wall, identifying and separating clustered or overlapped colonies, and counting colonies by using connected region labelling, distance transform, and watershed algorithms, etc. A user-friendly graphic user interface was also developed for the proposed method. The relative error and counting time of the automatic counting method were compared with those of manual counting. The results showed that the relative error of the automatic counting method was -7.4%similar to + 8.3%, with average relative error of 0.2%, and the time used for counting colonies on each agar plate was 11-21 s, which was 15-75% of the time used in manual counting, depending on the numbers of colonies on agar plates. The proposed system and automatic counting method demonstrate promising performance in terms of precision, and they are robust and efficient in terms of labor- and time-ssavings.