Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

DistancePPG: Robust non-contact vital signs monitoring using a camera

Using radio-frequency (RF) sensing techniques for human posture recognition has attracted growing interest due to its advantages of pervasiveness, contact-free observation, and privacy protection. Conventional RF sensing techniques are constrained by their radio environments, which limit the number of transmission channels to carry multi-dimensional information about human postures. Instead of passively adapting to the environment, in this paper, we design an RF sensing system for posture recognition based on reconfigurable intelligent surfaces (RISs). The proposed system can actively customize the environments to provide desirable propagation properties and diverse transmission channels. However, achieving high recognition accuracy requires the optimization of RIS configuration, which is a challenging problem. To tackle this challenge, we formulate the optimization problem, decompose it into two subproblems, and propose algorithms to solve them. Based on the developed algorithms, we implement the system and carry out practical experiments. Both simulation and experimental results verify the effectiveness of the designed algorithms and system. Compared to the random configuration and non-configurable environment cases, the designed system can greatly improve the recognition accuracy.

Reconfigurable Intelligent Surface Based RF Sensing: Design, Optimization, and Implementation

This paper presents a millimeter-wave radar in the 24-GHz ISM band for detection, tracking, and classification of human targets. Linear frequency modulation of the transmit signal and two receive antennas enable distance and angle measurements, respectively. Multiple consecutive frequency chirps are used for target velocity calculation. Hardware as well as firmware concepts of the proposed system are described in detail. Various algorithms for human detection and tracking are investigated and combined into a new signal processing routine optimized for compactness and low power to run on a microcontroller. In addition, a novel Doppler-compensated angle-of-arrival estimation method and a one-class support vector machine for human classification are proposed to further enhance the human detection and tracking performance. The achieved performances of the designed hardware and the implemented algorithm are verified in extensive measurements. The distance and angle errors of the realized radar sensor are at most 25 cm along a measurement range of 18 m and 10° for a two-sided angle sweep of 65°, respectively. The achieved range resolution is 0.9 m. The dedicated verifications of the most important signal processing routines are presented to verify their functionality and experiments with several human targets that illustrate the performance and limits of the overall tracking algorithm. It is shown that range, velocity, and angle of up to five humans are correctly detected and tracked. The presented one-class classifier successfully distinguishes the human targets from other quasi-static targets, such as trees and shadowing effects of human subjects on walls.

Human Target Detection, Tracking, and Classification Using 24-GHz FMCW Radar

The trend toward technology ubiquity in human life is constantly increasing and the same tendency is clear in all technologies aimed at human monitoring. In this framework, several smart home system architectures have been presented in literature, realized by combining sensors, home servers, and online platforms. In this paper, a new system architecture suitable for human monitoring based on Wi-Fi connectivity is introduced. The proposed solution lowers costs and implementation burden by using the Internet connection that leans on standard home modem-routers, already present normally in the homes, and reducing the need for range extenders thanks to the long range of the Wi-Fi signal. Since the main drawback of the Wi-Fi implementation is the high energy drain, low power design strategies have been considered to provide each battery-powered sensor with a lifetime suitable for a consumer application. Moreover, in order to consider the higher consumption arising in the case of the Wi-Fi/Internet connectivity loss, dedicated operating cycles have been introduced obtaining an energy savings of up to 91%. Performance was evaluated: in order to validate the use of the system as a hardware platform for behavioral services, an activity profile of a user for two months in a real context has been extracted.

https://www.mdpi.com/2079-9292/7/9/200/pdf

A Plug and Play IoT Wi-Fi Smart Home System for Human Monitoring

This paper reports a thorough overview on the last developments concerning the vital sign detection and the human localization employing the multiple-input-multiple-output (MIMO) technology. The wireless motion and vital sign detection represents an outstanding research area aimed at monitoring the health conditions of human subjects and at detecting their presence in different environments with minimal concern. MIMO radars exhibit several interesting advantages over conventional single-input-single-output architectures mainly related to their angle detection capabilities and enhanced signal-to-noise ratio. This paper describes the main features and details the operating principles of MIMO technology. Thereafter, it summarizes the state-of-the-art of the available solutions with the purpose of fueling the research activities on this hot topic.

https://www.mdpi.com/2079-9292/9/9/1497/pdf

A Review on Biomedical MIMO Radars for Vital Sign Detection and Human Localization

The elderly are constantly in danger of falling and injuring themselves without anyone realizing it. A safety-monitoring system based on microwaves can ease these concerns. The authors have proposed safety-monitoring systems that use multiple-input multiple-output (MIMO) radar to localize persons by capturing their biological activities such as respiration. However, our studies to date have focused on localization, which is easier to achieve than an estimation of human postures. This paper proposes a human posture identification scheme based on height and a Doppler radar cross section (RCS) as estimated by a MIMO array. This scheme allows smart home applications to dispense with contact and wearable devices. Experiments demonstrate that this method can identify the supine position (i.e., after a fall) with 100% accuracy, and the average identification rate is 95.0%.

https://www.mdpi.com/2079-9292/7/3/37/pdf

Human Posture Identification Using a MIMO Array

This paper proposes a method that uses bistatic MultipleInput Multiple-Output (MIMO) radar to locate living-bodies. In this method, directions of living-bodies are estimated by the MUltiple SIgnal Classification (MUSIC) method at the transmitter and receiver, where the Fourier transformed virtual Single-Input Multiple-Output (SIMO) channel matrix is used. Body location is taken as the intersection of the two directions. The proposal uses a single frequency and so has a great advantage over conventional methods that need a wide frequency band. Also, this method can be used in multipath-rich environments such as indoors. An experiment is performed in an indoor environment, and the MIMO channels yielded by various subject numbers and positions are measured. The result indicates that the proposed method can estimate multiple living-body locations with high accuracy, even in multipath environments. key words: MIMO, living-body location, DOA estimation, microwave sensor, array antenna

Estimating Living-Body Location Using Bistatic MIMO Radar in Multi-Path Environment

This paper presents some of the human-monitoring techniques using multiple-input multiple-output (MIMO) radar. Our MIMO radar estimates the location of the human body, where the subject does not have to ware any devices. The accurate localization method is briefly described and its results are also presented. Furthermore, the posture estimation method is also presented, and the results validate the presented method well works even with the limited information, which only includes the height and radar cross section of the subject.

Human Monitoring Using MIMO Radar

This paper demonstrates the measurement results showing the effectiveness of a fast human localization and tracking method suitable for multi-path environment. In this method, a time-differential channel is calculated by taking difference among the observed channels corresponding to biological activity. The human locations are estimated by two-dimensional MUSIC (MUltiple SIgnal Classification) correlation matrix calculated from the time-differential channels. The experiments were carried out in an indoor environment, and the results show that the target locations and trajectories can be estimated by the temporal-subtracting technique. The averaged tracking error is 0.29 m in this experiment.

Evaluation of fast human localization and tracking using MIMO radar in multi-path environment

A fast living-body localization algorithm using time-differential channel suitable for multipath environments is introduced. In this method, a time-differential channel is calculated from the difference among the observed channels that correspond to known biological activities such as respiration and heartbeat. The living-body locations are estimated by applying 2-D multiple signal classification to the correlation matrix calculated from the time-differential channels. Experiments were carried out in an indoor environment, and the results show that the proposed localization algorithm could well estimate multiple device-free living-bodies simultaneously. The experimental results demonstrate that the locations of up to three persons could be estimated faster, within a few seconds, and more accurately than the conventional method. One, two or three targets yield 90% values of localization errors of 0.23, 0.41, and 1.29 m, respectively.

Dai Sasakawa

Papers

Human Posture Identification Using a MIMO Array

Estimating Living-Body Location Using Bistatic MIMO Radar in Multi-Path Environment

Human Monitoring Using MIMO Radar

Evaluation of fast human localization and tracking using MIMO radar in multi-path environment

Fast Living-Body Localization Algorithm for MIMO Radar in Multipath Environment