Breathing Rhythm Analysis in Body Centric Networks

Respiratory rhythm is the marker of respiratory diseases. A compromised respiratory system can be life threatening and potentially cause damage to other organs and tissues. However, most people do not realize the importance of respiratory rhythm detection because of expensive and limited medical conditions. In this paper, we present a noncontact and economically viable respiratory rhythm-detection system using S-band sensing technique. The system leverages microwave sensing platform to capture the minute variations caused by breathing. Subsequently, we implement data preprocessing and respiratory rate estimation for acquired wireless data to achieve respiratory rhythm detection. The experimental results not only validate the feasibility of respiratory rhythm detection using S-band sensing technique but also demonstrate that the S-Breath system provides a good performance

Hand palm local channel characterization for millimeter-wave body-centric applications

The body-centric wireless channel characterization mostly utilizes whole body models. However, localized channels for body parts consistently interacting with the wireless device have their own importance. This paper attempts to characterize the hand palm local channel through experimental measurements at three millimeter-wave frequency bands of 27-28 GHz, 29-30 GHz, and 31-32 GHz. Five human subjects are used in this study. Net body loss is found to be 3dB for different subjects with subject-specific and varying palm shape size is found to be the primary affecting source. The repeatability of the on-body propagation measurements is found to be within 10% of variance

Contactless finger tapping detection at C band

The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities

A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection Systems

The emerging non-wearable fall detection systems rely on processing radio waves reflected off the body of the home user who has no active interaction with the system, increasing the user privacy and acceptability. This paper proposes a nonstationary channel model that is important for the development of such systems. A three-dimensional stochastic trajectory model is designed to capture targeted mobility patterns of the home user. The model is featured with a forward fall mechanism, which is actuated at a random point along the path. A transmitter emits radio waves throughout an indoor propagation environment, while a receiver collects fingerprints of the scattering objects on the emitted waves. The corresponding radio channel is modelled by a process capturing the time-variant Doppler effect caused by the home occupant. The time-frequency behaviour of the non-stationary channel is studied by computing the Doppler power spectral density and by performing spectrogram analysis. The instantaneous mean Doppler shift and Doppler spread are derived and simulated. The model is confirmed with experimental results performed at 5.9 GHz. The results are insightful for developing reliable fall detection algorithms, while the model is useful for studying the impact of different walking/falling patterns on the overall fall detection system performance.A Non-Stationary Channel Model for the Development of Non-Wearable Radio Fall Detection SystemsacceptedVersionNivå

A systematic review of non-contact sensing for developing a platform to contain COVID-19

The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

An integrated open source acoustofluidic platform using surface acoustic waves for biomedical applications

Surface acoustic wave (SAW) devices using thin film technology are increasingly used in lab-on-a-chip, point-of-care and a wide variety of biomedical applications, due to their multi-functionalities and low cost. These thin film devices not only have both acoustofluidic sensing and actuation functions, but also have been produced with commonly used semiconductor manufacturing techniques. These allow the acoustic devices to be made on many different substrates, such as aluminium plate and foils, glass, silicon, polymers and plastics, which provide a wide variety of properties enabling many new directions and opening up new applications. However, acoustic wave technology still requires benchtop lab equipment and experienced operators to utilise these SAW devices because of a lack of hardware integration and autonomous control, resulting in a higher-cost system than the proposed platform. Most SAW interfacing setups are bulky and complex to use. There are currently many studies exploring the uses of mobile phones, cameras and attempting to use open-source electronics to generate and control acoustic waves. In this thesis, we combine SAW microfluidics and sensing with Raspberry-Pi hardware, making a full use of its digital imaging capabilities. This thesis focuses on integrating surface acoustic wave devices and open-source hardware and software to overcome the challenges with a digitally controlled acoustofluidic platform. The aim of this modular platform is to perform acoustofluidic functions autonomously, such as droplet transportation, mixing, heating and sensing. The basis of this platform is a Raspberry Pi, together with piezoelectric thin films on metallic substrates, 3D printed housing and additional electronics for SAW device control. The setup is then used to demonstrate these functions applied in a variety of biomedical applications, such as disease diagnostics, breathing disorder monitoring and cell culturing