Maria Lindén

Electrical characteristics of conductive yarns and textile electrodes for medical applications

Clothing with conductive textiles for health care applications has in the last decade been of an upcoming research interest. An advantage with the technique is its suitability in distributed and home health care. The present study investigates the electrical properties of conductive yarns and textile electrodes in contact with human skin, thus representing a real ECG-registration situation. The yarn measurements showed a pure resistive characteristic proportional to the length. The electrodes made of pure stainless steel (electrode A) and 20% stainless steel/80% polyester (electrode B) showed acceptable stability of electrode potentials, the stability of A was better than that of B. The electrode made of silver plated copper (electrode C) was less stable. The electrode impedance was lower for electrodes A and B than that for electrode C. From an electrical properties point of view we recommend to use electrodes of type A to be used in intelligent textile medical applications.

Evaluation of the android-based fall detection system with physiological data monitoring

Aging population is considered to be major problem in modern healthcare. At the same time, fall incidents often occur among elderly and cause serious injuries affecting their independent living. This paper proposes a framework which uses mobile phone technology together with physiological data monitoring in order to detect falls. The system carries out collecting, storing and processing of acceleration data with further alarm generating and transferring all the measurements to remote caregiver. To perform evaluation, an experimental setup involving novice ice-skaters were carried out to obtain realistic fall data and examine the effects of falling on physiological parameters. A fall detection algorithm has been designed therefore to cope with large variations of movement in the torso. The online algorithm operating showed performance results of 90% specificity, 100% sensitivity and 94% accuracy.

Blood flow measurements at different depths using photoplethysmography and laser Doppler techniques

Background/purpose: This study has evaluated a multi‐parametric system combining laser Doppler flowmetry and photoplethysmography in a single probe for the simultaneous measurement of blood flow at different depths in the tissue. This system will be used to facilitate the understanding of pressure ulcer formation and in the evaluation of pressure ulcer mattresses.

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

Existence of Tissue Blood Flow in Response to External Pressure in the Sacral Region of Elderly Individuals - Using an Optical Probe Prototype

Microcirculation (2010) 17, 311–319. doi: 10.1111/j.1549‐8719.2010.00027.x

Electrical Properties of Textile Electrodes

In this study we aim to explain the behavior of textile electrodes due to their construction techniques. Three textile electrodes were tested for electrode impedance and polarization potentials. The multifilament yarn (A) is favorable for its low thread resistance. Although, when knitted into electrodes, the staple fiber yarn (B) showed a comparable and satisfiable electrode impedance. The multifilament yarn had however a lower polarization potential drift then the other specimens. The monofilament yarn (C) had high electrode impedance and varying mean polarization potentials due to its conductive material and small contact area with the skin.

A Bluetooth Radio Energy Consumption Model for Low-Duty-Cycle Applications

This paper presents a realistic model of the radio energy consumption for Bluetooth-equipped sensor nodes used in a low-duty-cycle network. The model is based on empirical energy consumption measurements of Bluetooth modules. This model will give users the possibility to optimize their radio communication with respect to energy consumption while sustaining the data rate. This paper shows that transmission power cannot always be directly related to energy consumption. Measurements indicate that, when the transmission power ranges from -5 to +10 dBm, the difference in consumed energy can be detected for each transmission peak in the sniff peak. However, the change is negligible for the overall energy consumption. The nonlinear behavior of the idle state for both master and slave when increasing the interval and number of attempts is presented. The energy consumption for a master node is in direct relation to the number of slaves and will increase by approximately 50% of the consumption of one slave per additional slave, regardless of the radio setting.

A Systematic Review of Wearable Patient Monitoring Systems --- Current Challenges and Opportunities for Clinical Adoption

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.

Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment

Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.

A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet

In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97dB and 0.02 respectively and a significantly higher correlation coefficient (p<0.05).