Krzysztof Czuszynski

Interaction with medical data using QR-codes

Bar-codes and QR-codes (Quick Response) are often used in healthcare. In this paper an application of QR-codes to exchange of laboratory results is presented. The secure data exchange is proposed between a laboratory and a patient and between a patient and Electronic Health Records. Advanced Encryption Standard was used to provide security of data encapsulated within a QR-code. The experimental setup, named labSeq is described. Additionally, a mobile application which enables decoding and storage of the enciphered information is presented. This work is also a preliminary research in supplying electronic glasses device, eGlasses, a functionality to process data from graphical patterns. In this paper a mobile phone is used as a simulator of electronic glasses. The exchange of data between the mobile application and Electronic Health Records is based on Health Level 7 standards or using JavaScript Object Notation. This enables fast integration of data between a patients device and the information system used by a healthcare professional.

Application of smart glasses for fast and automatic color correction in health care

In recent years different applications of smart glasses in health care have been proposed. In this paper we present the experiments related to automatic color correction using smart glasses platform developed within the eGlasses project. The color pattern is proposed and tested enabling the automatic detection of the pattern and automatic correction of colors. Additionally, the method for encoding and decoding of patient ID in the color pattern is presented. This enables automatic data integration using smart glasses connected to Hospital Information System or similar systems.

Comparison of active proximity radars for the wearable devices

Two methods of object position and movement estimation in relation to the user of smart glasses were investigated. An active infrared and ultrasonic methods of the obstacle detection were presented and compared. Application of these methods depend on active transducers type (physical medium used), geometry and surface properties of detected objects and their movement direction and speed. In the article properties of both detectors were compared and applicability of both methods in mobile, battery operated environment such as eGlasses platform were compared.

Sympathetic Activation Does Not Affect the Cardiac and Respiratory Contribution to the Relationship between Blood Pressure and Pial Artery Pulsation Oscillations in Healthy Subjects

Introduction Using a novel method called near-infrared transillumination backscattering sounding (NIR-T/BSS) that allows for the non-invasive measurement of pial artery pulsation (cc-TQ) and subarachnoid width (sas-TQ) in humans, we assessed the influence of sympathetic activation on the cardiac and respiratory contribution to blood pressure (BP) cc-TQ oscillations in healthy subjects. Methods The pial artery and subarachnoid width response to handgrip (HGT) and cold test (CT) were studied in 20 healthy subjects. The cc-TQ and sas-TQ were measured using NIR-T/BSS; cerebral blood flow velocity (CBFV) was measured using Doppler ultrasound of the left internal carotid artery; heart rate (HR) and beat-to-beat mean BP were recorded using a continuous finger-pulse photoplethysmography; respiratory rate (RR), minute ventilation (MV), end-tidal CO2 (EtCO2) and end-tidal O2 (EtO2) were measured using a metabolic and spirometry module of the medical monitoring system. Wavelet transform analysis was used to assess the relationship between BP and cc-TQ oscillations. Results HGT evoked an increase in BP (+15.9%; P<0.001), HR (14.7; P<0.001), SaO2 (+0.5; P<0.001) EtO2 (+2.1; P<0.05) RR (+9.2%; P = 0.05) and MV (+15.5%; P<0.001), while sas-TQ was diminished (-8.12%; P<0.001), and a clear trend toward cc-TQ decline was observed (-11.0%; NS). CBFV (+2.9%; NS) and EtCO2 (-0.7; NS) did not change during HGT. CT evoked an increase in BP (+7.4%; P<0.001), sas-TQ (+3.5%; P<0.05) and SaO2(+0.3%; P<0.05). HR (+2.3%; NS), CBFV (+2.0%; NS), EtO2 (-0.7%; NS) and EtCO2 (+0.9%; NS) remained unchanged. A trend toward decreased cc-TQ was observed (-5.1%; NS). The sas-TQ response was biphasic with elevation during the first 40 seconds (+8.8% vs. baseline; P<0.001) and subsequent decline (+4.1% vs. baseline; P<0.05). No change with respect to wavelet coherence and wavelet phase coherence was found between the BP and cc-TQ oscillations. Conclusions Short sympathetic activation does not affect the cardiac and respiratory contribution to the relationship between BP—cc-TQ oscillations. HGT and CT display divergent effects on the width of the subarachnoid space, an indirect marker of changes in intracranial pressure.

Septic safe interactions with smart glasses in health care.

In this paper, septic safe methods of interaction with smart glasses, due to the health care environment applications consideration, are presented. The main focus is on capabilities of an optical, proximity-based gesture sensor and eye-tracker input systems. The design of both interfaces is being adapted to the open smart glasses platform that is being developed under the eGlasses project. Preliminary results obtained from the proximity sensor show that the recognition of different static and dynamic hand gestures is promising. The experiments performed for the eye-tracker module shown the possibility of interaction with simple Graphical User Interface provided by the near-to-eye display. Research leads to the conclusion of attractiveness of collaborative interfaces for interaction with smart glasses.

Estimation of the amplitude of the signal for the active optical gesture sensor with sparse detectors

In this paper we deal with the problem of precise gesture recognition for the active optical proximity sensor with sparse 8 photodiodes. We particularly focus on developing the method of estimating the real, usually not observable, maximum signal value representing maximum intensity of light reflected from an obstacle present in the front of the sensor. Different configurations of the fingers were used as an obstacle. The Monte Carlo simulations were performed in order to cognize the accurate pattern created by fingers of considered configurations. The accurate description of curves representing finger profiles was obtained after applying the least squares method. The results of analyzes proved that finger arrangement configurations, particularly 1 finger and 2 jointed fingers, can be considered as sources of Gaussian like shapes on the face of the sensor. Therefore, data measured by 8 sparse photodiodes of the active optical gesture sensor were compared to normal distribution curves. The results of the comparison allow normalization of the measured data and the estimation of the real, usually not observable, maximum signal value. The results and discussion sections of the paper present the average detection accuracy, advantages and limitations of the proposed approach.

Interactions using passive optical proximity detector

In this paper we evaluated the possible application of a passive, optical sensor as an interface for human-smart glasses interactions. The designed proximity sensor is composed of set of photodiodes and the appropriate hardware and software components. First, experiments were performed for the estimations of such parameters as distance to an object, its width and velocity. Achieved results were satisfactory. Therefore, next, a set of static and dynamic hand gestures was proposed together with the related recognition method. The method was verified during experiments with volunteers. Experiments have shown that in appropriate lighting conditions it is possible to detect static and dynamic simple hand gestures using inexpensive and computationally efficient sensor. These features enable the use of such a sensor as one of the smart glasses interfaces.

Estimation of respiration rate using an accelerometer and thermal camera in eGlasses

Respiration rate is a very important vital sign. Different methods of respiration rate measurement or estimation have been developed. However, especially interesting are those that enable remote and unobtrusive monitoring. In this study, we investigated the use of smart glasses for the estimation of respiration rate especially useful for indoors applications. Two methods were analyzed. The first one is based on measurements of respiration-related body movements using an accelerometer. The second one uses the thermal camera to observe temperature changes in the nostril region. For both methods signals were extracted, filtered and processed using two different respiration rate estimators. Both methods were validated during experiments with the participation of volunteers using the respiration belt as a reference measurement method. Results proved that for both methods it is possible to reliable estimate the respiration rate with Root Mean Square Error lower than 2 breaths per minute, which is sufficient for medical screening.

Pose classification in the gesture recognition using the linear optical sensor

Gesture sensors for mobile devices, which have a capability of distinguishing hand poses, require efficient and accurate classifiers in order to recognize gestures based on the sequences of primitives. Two methods of poses recognition for the optical linear sensor were proposed and validated. The Gaussian distribution fitting and Artificial Neural Network based methods represent two kinds of classification approaches. Three types of hand poses, differing in the number of fingers joined together, were investigated. The reflected light intensity pattern originated by hand located closely to the sensor was parameterized into 14 features. The change of reflection pattern originated by hand dislocation was reduced by application of two variable functions in the first of the methods. A one and two hidden layers topologies were considered in the neural network related approach. Both methods were designed with the use of a training set of samples and validated with another (testing) set. The results present the average poses recognition rate of 81.19% for Gaussian distribution fitting and 90.02% for ANN based method.

Semi complex navigation with an active optical gesture sensor

This paper presents the methods of diversified touchless interactions between a user and a mobile platform utilizing the optical gesture sensor. The sensor uses 8 photodiodes to measure the reflected light in the active mode (using embedded LEDs) or it measures shadows caused by fingers in the passive mode. Several algorithms were implemented: automatic mode switching, adaptive illumination level compensation, resolution improvements using model to data fitting, etc. The embedded processor delivers recognized static or dynamic gesture codes, location of fingers, etc. Thanks to the sparse construction of the sensor and number of the detectors, it handles a few discrete but also continuous gestures. The most important features of the sensor are: low power and computational requirements in comparison to vision based gesture recognition systems and that it is a septic safe interface. It was embedded into smart glasses platform.