S T Beardsmore-Rust

Remote detection of human electrophysiological signals using electric potential sensors

We describe the measurement of human electrophysiological and movement signals remotely from a seated subject. An ultrahigh impedance electric potential sensor, designed specifically to reject external noise, is used to measure the electric field at distances of up to 40cm from the surface of the body. The sensor is able to provide continuous data acquisition, at full sensitivity, without saturation by external noise sources. Respiration and heart signals are seen simultaneously and are separated using digital filtering techniques. All of the results reported were obtained in an open unshielded environment in close proximity to line operated computer equipment.

Adaptive Electric Potential Sensors for smart signal acquisition and processing

Current applications of the Electric Potential Sensor operate in a strongly (capacitively) coupled limit, with the sensor physically close to or touching the source. This mode of operation screens the sensor effectively from the majority of external noise. To date however the full capability of these sensors operating in a remote mode has not been realised outside of a screened environment (Faraday cage). This paper describes the results of preliminary work in tailoring the response of the sensors to particular signals and so reject background noise, thereby enhancing both the dynamic range and signal to noise ratio significantly.

Imaging of charge spatial density on insulating materials

We describe the measurement of spatial charge distribution, using a new non-invasive technique. This measurement, based on a 16-element array of ultra-high impedance electric field sensors, is capable of producing both quantitative results for the total amount of surface charge present, as well as imaging the charge to produce plots representing spatial charge distribution. We calibrate the measurement against a conventional induction field meter charge measurement which discharges the sample. Further to this, we show that our technique has no discharging effect on the sample and that therefore it is possible to observe the discharging of insulating materials over periods of several days.

Position and movement sensing at metre standoff distances using ambient electric field

We describe a system for the measurement of changes in electric field which occur as a result of the movement of people, or objects, in ambient electric fields with standoff distances of several metres. A passive sensor system is used to measure the changes in electric field which are due to several different mechanisms. From this we are able to extract presence, movement and position information with a positional accuracy of ∼10 cm. Furthermore, by examining the disturbances in ambient ac fields, such as those created by domestic electricity networks, we show that it is possible to recover static field information with a sensor that lacks dc sensitivity. In this way, we demonstrate that tracking of individuals within large room-scale spaces is possible. As a simple, passive, undetectable technique, with no line of sight requirement, these measurements open up new possibilities in security, telehealth and human computer interfacing applications.

Signal specific electric potential sensors for operation in noisy environments

Limitations on the performance of electric potential sensors are due to saturation caused by environmental electromagnetic noise. The work described involves tailoring the response of the sensors to reject the main components of the noise, thereby enhancing both the effective dynamic range and signal to noise. We show that by using real-time analogue signal processing it is possible to detect a human heartbeat at a distance of 40 cm from the front of a subject in an unshielded laboratory. This result has significant implications both for security sensing and biometric measurements in addition to the more obvious safety related applications.

Detecting electric field disturbances for passive through-wall movement and proximity sensing

In this paper we outline the application of a novel electric field sensor technology, developed and patented at the University of Sussex, to the sensing of movement and proximity, using a technique which is generally unaffected by the presence of walls and other structures. This is achieved by monitoring electric field disturbances which occur when a large dielectric object, such as a human or animal body, is moved through the ambient electric field. These sensors detect, passively, changes in spatial potential (electric field) created by a capacitively coupled electric field. To date we have already demonstrated the potential applications of these devices, in principle, across many areas of interest, including body electrophysiology, novel nuclear magnetic resonance (NMR) probes, non destructive testing of composite materials as well as the detection of a heart beat from distances of up to 40 cm. Here we show how, with multiple sensors in a variety of spatial arrangements, it is possible to use simple signal processing and analysis in Labview to detect movement, give an indication of direction and speed as well as track position within an open environment.

Passive tracking of targets using electric field sensors

We have reported previously on the use of a novel Electric Potential Sensor, developed and patented at the University of Sussex, for remote monitoring of life signs and through-wall sensing of movement and proximity. In this paper we present the data obtained using a sparse (4-element) array of sensors to image a volume of space for target movements. This is achieved by passive monitoring of the disturbances which result from the movement of a dielectric object through the ambient electric field. Numerical computation is used to simulate the expected sensor responses for a given pattern of movement and comparison with these simulations allows the trajectory to be followed. With this 4-element array, it is possible to track the movement of a single subject, for example an intruder, or the lone occupant of a room. However, with the addition of just a few extra sensors, it is possible to resolve the ambiguities caused by multiple targets. The advantage of this approach over competing technologies such as radar, for through-wall surveillance and tracking, is that the method is passive. It requires no excitation field or probe signal and relies instead on the ambient static electric field which exists between the ionosphere and the surface of the Earth. It therefore only works well if the array is not obstructed by earthed conducting materials, in common with the other technologies. On the other hand, the passive nature of the technique provides a low power system which is potentially undetectable.

Imaging the time sequence of latent electrostatic fingerprints

Biometric identification for forensic investigations and security continues to depend on classic fingerprinting in many instances. Existing techniques rely on either visible deposits or hidden (latent) fingerprints resulting from the transfer of residues from the finger to the surface. However, one of the limitations of classic fingerprinting, for use as forensic evidence, is in determining a time sequence of events. It is extremely difficult to establish a timeline, from fingerprint evidence alone. We present the capability of a new technique which images the electrical charge deposited by tribocharging when a finger contacts an electrically insulated surface. The method is applicable to insulating surfaces and has been tested on PVC, PTFE, Acetate and PVDF sheets. The latent electrostatic charge pattern is detected using a novel, microscopic, electric potential sensor. The sensor is capable of imaging static charge distributions non-invasively, with no discharging effect on the sample. We present data showing the decay of the charge image with time, over a period up to 14 days. This capability has two major implications. First this technique does not suffer from ambiguities caused by a history of old fingerprints and second it has the potential to allow the time sequence of recent charge fingerprint images to be determined.