FEATURE NON CONTACT MEASUREMENT & INSPECTION
Researchers from Coventry University and University College London have incorporated technology from National Instruments to create a passive WiFi sensing system that could have applications in healthcare and beyond. Dr. Bo Tan, Qingchao Chen, Dr. Kevin Chetty and Prof. Karl Woodbridge explain how the technology can impact on people’s lives
ealthcare has become one of the biggest social and economic issues of
our time. The need to assist people with disabilities, chronic disease, dementia, and mental health issues places increasing demands on limited resources for around- the-clock monitoring of activity, health, and well-being, especially within residential healthcare.
Figure 1: Passive WiFi sensing for in-home activity sensing
into passive WiFi sensing systems, and led them to adopt LabVIEW and USRP (Universal Software Radio Peripheral) for rapid prototyping. The concept of passive WiFi sensing
PASSIVE WIFI SENSING IN HEALTHCARE H
A team of researchers from Coventry
University and University College London used NI’s LabVIEW and USRP to create a passive WiFi sensing system that can detect body movements and vital signs of a subject through walls and without any physical contact. The detailed analysis of the WiFi signals that reflect off a patient reveals patterns, which can be served up to gesture recognition libraries and machine learning systems for classification of activities and model lifestyle behaviour for healthcare applications. The primary aim of health-related
artificial intelligence (AI) applications is to analyse the relationships between prevention or treatment techniques and patient outcomes. Such systems need to work with accurate signal data about instant and long-term activities that make up an individual’s pattern of life information. Engineers are currently exploring solutions for the following challenges in residential healthcare: vital signs; life threatening events; daily activities; and chronic activity level awareness. The widely adopted detection methods currently used within care homes include wearable devices, camera-based vision systems, and ambient sensors. However, these established options have major drawbacks (respectively, physical discomfort, privacy concerns, and limited detection accuracy). There is an urgent requirement to
develop novel monitoring solutions, which are contactless, accurate and minimally invasive. This inspired the team’s research
14 MARCH 2018 | INSTRUMENTATION
for residential healthcare is a natural extension of research at University College London, which proved the concept of passive WiFi radar. Here, the term passive refers to the fact that users do not need to actively transmit a wireless signal to receive the radar echo. Instead, the passive WiFi prototype, which was based on NI software defined radio (SDR) solutions, leverages the wireless signals that already swamp our urban airways. Because passive WiFi radar is “receive only”, it is low power, unobtrusive and completely undetectable. This is a major benefit to military and counterterrorism scenarios. NI technologies best fit the team’s research needs. The NI SDR solution was used to transition from concept to prototype to deployment faster than alternative approaches and is very versatile. The team could repurpose the original prototype for entirely new applications, including health and activity monitoring in retirement and nursing homes. However, scaling the prototype to fit the needs of residential healthcare required advancements in two key areas: signal processing and machine learning.
SIGNAL PROCESSING Passive WiFi sensing is a receive-only system that measures the dynamic WiFi signal changes caused by moving indoor objectives. The indoor multiple path propagation negatively impacts the wireless communication, but gives an opportunity for interpreting human activities. Due to the dynamic movement
Figure 2: USRP- based concept system design and architecture of hardware and software
of a subject, the dynamic path presents time variation on the angle of arriving (AoA, ) and propagation delay () that correlate with the subject’s movements. When taking an incisive look of and , the phase change of the receiving signal can present all of them. Frequency can be used to measure phase changing rate during the measurement duration and Doppler shift to identify movements. It is possible to discern real-time, high- resolution Doppler shifts for a given duration using batch processing boosted cross ambiguity function (CAF) analysis. The team also use the phase of each batch to identify small displacements of a subject, which is often used for inconspicuous body movement like breathing. Most commercial wireless network interface cards cannot deliver raw RF signal samples, which is why the researchers chose USRP and LabVIEW software to capture, process and interpret the signals.
MACHINE LEARNING The team can easily interpret some captured signals. For example, they can directly link the periodic change of batch phase with respiration rate. However, others may be difficult to understand visually. An example is the Doppler-time spectral map associated with gestures like picking things up or sitting down. Thus, the researchers introduced machine learning to discover the link between the Doppler-time spectral map and physical activities. In practice, the team tested principle component analysis (PCA) and singular value decomposition for Doppler- time spectral mapper feature extraction. Then the team feed the features to support vector machine and sparse representation classifier (SRC). The resulting classifiers show a
promising capability of recognising the daily activities from Doppler-time spectral map. Besides the classification of the instant activities, the machine learning method can also model the pattern of a resident’s lifestyle by interpreting the long-term passive WiFi activity data.
BUILDING THE PROOF-OF- CONCEPT SYSTEM To prove the concept, the researchers built a prototype system based on USRP SDR and LabVIEW. The USRP captures the raw
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