Daniel Teichmann

The MAIN Shirt: A Textile-Integrated Magnetic Induction Sensor Array

A system is presented for long-term monitoring of respiration and pulse. It comprises four non-contact sensors based on magnetic eddy current induction that are textile-integrated into a shirt. The sensors are technically characterized by laboratory experiments that investigate the sensitivity and measuring depth, as well as the mutual interaction between adjacent pairs of sensors. The ability of the device to monitor respiration and pulse is demonstrated by measurements in healthy volunteers. The proposed system (called the MAIN (magnetic induction) Shirt) does not need electrodes or any other skin contact. It is wearable, unobtrusive and can easily be integrated into an individuals daily routine. Therefore, the system appears to be a suitable option for long-term monitoring in a domestic environment or any other unsupervised telemonitoring scenario.

Noncontact Monitoring of Cardiorespiratory Activity by Electromagnetic Coupling

In this paper, the method of noncontact monitoring of cardiorespiratory activity by electromagnetic coupling with human tissue is investigated. Two measurement modalities were joined: an inductive coupling sensor based on magnetic eddy current induction and a capacitive coupling sensor based on displacement current induction. The systems sensitivity to electric tissue properties and its dependence on motion are analyzed theoretically as well as experimentally for the inductive and capacitive coupling path. The potential of both coupling methods to assess respiration and pulse without contact and a minimum of thoracic wall motion was verified by laboratory experiments. The demonstrator was embedded in a chair to enable recording from the back part of the thorax.

Non-contact monitoring techniques - Principles and applications

This work gives an overview about some non-contact methods for monitoring of physiological activity. In particular, the focus is on ballistocardiography, capacitive ECG, Infrared Thermography, Magnetic Impedance Monitroing and Photoplethymographic Imaging. The principles behind the methods are described and an inside into possible medical applications is offered.

A Bendable and Wearable Cardiorespiratory Monitoring Device Fusing Two Noncontact Sensor Principles

A mobile device is presented for monitoring both respiration and pulse. The device is developed as a bendable/flexible inlay that can be placed in a shirt pocket or the inside pocket of a jacket. To achieve optimum monitoring performance, the device combines two sensor principles, which work in a safe noncontact way through several layers of cotton or other textiles. One sensor, based on magnetic induction, is intended for respiratory monitoring, and the other is a reflective photoplethysmography sensor intended for pulse detection. Because each sensor signal has some dependence on both physiological parameters, fusing the sensor signals allows enhanced signal coverage.

Human motion classification based on a textile integrated and wearable sensor array

A system for classification of motion patterns is presented based on a non-contact magnetic induction monitoring device. This device is textile integrated, wearable, and able to measure pulse and respiratory activity. The proposed classifiers are a neural network, support vector machine, and a decision tree algorithm generated by bootstrap aggregating. Their performance is compared using a data set comprising five different types of motion patterns. In addition, the dependence of the misclassification error on the input sample length is investigated. The features used for classification were based on information derived by discrete wavelet transform and on lower and higher order statistical measures. With the presented magnetic induction device, all tested classifiers were able to classify the defined motion pattern with an accuracy of over 93%. The proposed bootstrap aggregating decision tree algorithm produces the best classification performance (accuracy of 96%). The support vector machine classifier shows the least dependence on the sample length.

System Description and First Application of an FPGA-Based Simultaneous Multi-Frequency Electrical Impedance Tomography

A new prototype of a multi-frequency electrical impedance tomography system is presented. The system uses a field-programmable gate array as a main controller and is configured to measure at different frequencies simultaneously through a composite waveform. Both real and imaginary components of the data are computed for each frequency and sent to the personal computer over an ethernet connection, where both time-difference imaging and frequency-difference imaging are reconstructed and visualized. The system has been tested for both time-difference and frequency-difference imaging for diverse sets of frequency pairs in a resistive/capacitive test unit and in self-experiments. To our knowledge, this is the first work that shows preliminary frequency-difference images of in-vivo experiments. Results of time-difference imaging were compared with simulation results and shown that the new prototype performs well at all frequencies in the tested range of 60 kHz–960 kHz. For frequency-difference images, further development of algorithms and an improved normalization process is required to correctly reconstruct and interpreted the resulting images.

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors

BackgroundExtracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes.MethodsWe present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case.ResultsExtracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min −1 (0.3 min −1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min −1 (0.4 min −1) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time.ConclusionsIt is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

Textile Integration of a Magnetic Induction Sensor for Monitoring of Cardiorespiratory Activity

In this paper the integration of a sensor for monitoring cardiorespiratory activity into a shirt is presented. The sensor works in a completely non-contacting way and is based on magnetic eddy current induction. By using a rather seldom approach based on a single coil that has been sewn onto the shirt, the electronic circuitry has been reduced to minimum. In this way it is possible to realize the whole electronic part on a small and flexible printed circuit board. The sensor’s potential for assessing respiration and pulse is demonstrated by measurements with a healthy volunteer.

Respiration monitoring based on magnetic induction using a single coil

Respiratory activity correlates with the conductivity distribution within the thorax. The change in conductivity can be monitored by impedance measurements based on eddy current induction without any need for contact between instrumentation and body. A system for magnetic induction measurements of the thorax using a single sensor-coil is presented. In contrast to most other known single coil respiration monitoring systems, the signal is amplitude and not frequency modulated. This renders the possibility of measuring with constant excitation frequencies.

SensInDenT-Noncontact Sensors Integrated Into Dental Treatment Units.

This paper presents the first system design (SensInDenT) for noncontact cardiorespiratory monitoring during dental treatment. The system is integrated into a dental treatment unit, and combines sensors based on electromagnetic, optical, and mechanical coupling at different sensor locations. The measurement principles and circuits are described and a system overview is presented. Furthermore, a first proof of concept is provided by taking measurements in healthy volunteers under laboratory conditions.