Contactless finger tapping detection at C band

The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice

Noninvasive suspicious liquid detection using wireless signals

Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol

CHARACTERIZATION OF RF SIGNAL BEHAVIOR FOR THE USE IN INDOOR HUMAN PRESENCE DETECTION

ABSTRACT This paper presents a method or technique that identifies physical intrusion detection in an indoor environment that is based on received signal strength indicator (RSSI) on radio frequency identification. The objective of this paper is two folds. Firstly, is to characterize the signal behavior in an indoor environment using statistical measures. Secondly, is to identify the existence of a human presence inside a contained environment (e.g., room). The objective is to use simple means like the recorded readings of the received signal strength indicator (RSSI). The characterization was observed during three distinctive time intervals, namely; empty room, with human presence, and transitional period during link crossings. The experiment was repeatedly conducted for 5 minutes to validate the averaged results. In order to emulate real-life environment, experiments were conducted using Zigbee-compliant iris mote XM2110 and MIB510 programming boards with transmitter and receiver antennas, and interfaced using TinyOS software on Linux. Our results show that there are distinctive statistical features that can utilized as flags to classify the three cases stated above, empty room, occupied and link being crossed. These results motivate the design of alarm system to detect human presence using RSSI statistics only

An Approach to Finding Parking Space Using the CSI-based WiFi Technology

With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal’s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking

Channel State Information from pure communication to sense and track human motion: A survey

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology

Walking Speed Detection from 5G prototype System

While most RF-sensing approaches proposed in the literature rely on short-distance indoor point-to-point instrumentation, actual large-scale installation of RF sensing suggests the use of ubiquitously available cellular systems. In particular, the 5th generation of the wireless communication standard (5G) is envisioned as a universal communication means also for Internet of Things devices. This thesis presents an investigation of device-free environmental perception capabilities in a 5G prototype system in two cases; walking speed and human presence detection, and elaborate a comparison with the former case and acceleration sensing analysis. This thesis attempts to analyze the perception capabilities of 5G system in order to recognize human mostly common activities and presence detection near transceiver devices which the instrumentation exploits a device-free system capable of detect activities without carrying devices capitalizing on environmental RF-noise. This is done via the study of existing and related literature. After that, the implementation and evaluation of walking speed and presence detection is described in details. In addition, evaluation consists of utilizing a prototypical 5G system with 52 OFDM carriers over 12.48 MHz bandwidth at 3.45 GHz, which we consider the impact of the number and choice of channels and compare the recognition performance with acceleration-based sensing. It was concluded that in realistic settings with five subjects, accurate recognition of activities and environmental situations can be a reliable implicit service of future 5G installations

Fall Detection Using Channel State Information from WiFi Devices

Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme