The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Equipment and workers are two important resources in the construction industry. Performance monitoring of these resources would help project managers improve the productivity rates of const...

Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review

Device free human gesture recognition using Wi-Fi CSI: A survey

Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors

Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field

Recently, device-free human activity–monitoring systems using commercial Wi-Fi devices have demonstrated a great potential to support smart home environments. These systems exploit Channel State Information (CSI), which represents how human activities–based environmental changes affect the Wi-Fi signals propagating through physical space. However, given that Wi-Fi signals either penetrate through an obstacle or are reflected by the obstacle, there is a high chance that the housing environment would have a great impact on the performance of a CSI-based activity-recognition system. In this context, this paper examines whether and to what extent housing environment affects the performance of the CSI-based activity recognition systems. Activities in daily living (ADL)–recognition systems were implemented in two typical housing environments representative of the United States and South Korea: a wood-frame apartment (Unit A) and a reinforced concrete-frame apartment (Unit B), respectively. The experimental results show that housing environments, combined with various environmental factors (i.e., structural building materials, surrounding Wi-Fi interference, housing layout, and population density), generate a significant difference in the accuracy of the applied CSI-based ADL-recognition systems. This outcome provides insights into how such ADL systems should be configured for various home environments.

https://www.mdpi.com/1424-8220/19/5/983/pdf

The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study

Fine-grained occupant activity monitoring with Wi-Fi channel state information: Practical implementation of multiple receiver settings

The authors present a method for calculating a mooring line’s tension and angle of inclination at the anchor pad eye to analyze the behavior of a mooring line embedded in sand. They adopted the governing equation from a previous study for clay and reanalyzed the frictional and bearing resistances. The authors performed a number of centrifuge tests at two anchor depths while maintaining relative densities of 76 or 51% in the cylindrical test box to validate the proposed analytical method. The analytical solutions agreed well with the test results and the results of the simplified method. The authors conducted a parametric study to evaluate the effects of various factors on the behavior of an embedded mooring line in sand, including the depth of the attachment point, the internal friction angle of sand, the submerged unit weight of sand, the angle of the mooring line on the seabed, and the self-weight and nominal diameter of the mooring line. The results demonstrated that the proposed analytical met...

Profile and Frictional Capacity of a Mooring Line Embedded in Sand via Centrifuge Model Testing

Channel State Information (CSI) has been used as an alternative sensing source for monitoring occupant's activities indoors. While various approaches have been proposed to extract features from the CSI and classify activities, those features fail to yield the spatial-temporal aspects of activities. In this context, this study presents new approach to extract appropriate features from multiple receivers. Time-series CSI data collected from a WiFi receiver is converted into an image data by Short-Time Fourier Transform (STFT), and then such the image data from multiple receivers are combined into a large image data, so that it contains information about the spatial-temporal aspects and distinct patterns of the activities. Convolutional Neural Network (CNN) extracted miscellaneous features from the converted image data. The extracted features are then fed into Long Short-Term Memory (LSTM) to classify the activities. The proposed hybrid CNN-LSTM model offers over 95% accuracy for classifying the key activities in daily living (ADLs) (e.g., walking, eating, toileting, bathing, etc.). This approach also shows consistent performance in two different housing environments.

Hoonyong Lee

Papers

Detecting excessive load-carrying tasks using a deep learning network with a Gramian Angular Field

The Effects of Housing Environments on the Performance of Activity-Recognition Systems Using Wi-Fi Channel State Information: An Exploratory Study

Fine-grained occupant activity monitoring with Wi-Fi channel state information: Practical implementation of multiple receiver settings

Profile and Frictional Capacity of a Mooring Line Embedded in Sand via Centrifuge Model Testing

Exploiting Multiple Receivers for CSI-Based Activity Classification Using A Hybrid CNN-LSTM Model