Sleep studies in mice - open and closed loop devices for untethered recording and stimulation

Sleep is an important biological processes that has been studied extensively to date. Research in sleep typically involves mice experiments that use heavy benchtop equipment or basic neural loggers to record ECoG/EMG signals which are then processed offline in workstations. These systems limit the complexity of experiments that can be carried out to only simple open loop recordings, due to either the tethered setup used, which restricts animal movements, or the lack of devices that can offer more advanced features without compromising its portability. With rising popularity in exploring more physiological features that can affect sleep, such as temperature, whose importance has been highlighted in several papers [1][2][3] and advances in optogenetic stimulation, allowing high temporal and spatial neural control, there is now an unprecedented demand for experimental setups using new closed loop paradigms. To address this, this thesis presents compact and lightweight neural logging devices that are not only capable of measuring ECoG and EMG signals for core sleep analysis but also capable of taking high resolution temperature recordings and delivering optogenetic stimulus with fully adjustable parameters. Together with its embedded on-board automatic sleep stage scoring algorithm, the device will allow researchers for the first time to be able to quickly uncover the role a neural circuit plays in sleep regulation through selective neural stimulation when the animal is under the target sleep vigilance state. Original contributions include: the development of two novel multichannel neural logging devices, one for core sleep analysis and another for closed loop experimentation; the development and implementation of a lightweight, fast and highly accurate automatic on-line sleep stage scoring algorithm; and the development of a custom optogenetic coupler that is compatible with most current optogenetic setups for LED-Optical fibre coupling.Open Acces

Tutorial: A Versatile Bio-Inspired System for Processing and Transmission of Muscular Information

Device wearability and operating time are trending topics in recent state-of-art works on surface ElectroMyoGraphic (sEMG) muscle monitoring. No optimal trade-off, able to concurrently address several problems of the acquisition system like robustness, miniaturization, versatility, and power efficiency, has yet been found. In this tutorial we present a solution to most of these issues, embedding in a single device both an sEMG acquisition channel, with our custom event-driven hardware feature extraction technique (named Average Threshold Crossing), and a digital part, which includes a microcontroller unit, for (optionally) sEMG sampling and processing, and a Bluetooth communication, for wireless data transmission. The knowledge acquired by the research group brought to an accurate selection of each single component, resulting in a very efficient prototype, with a comfortable final size (57.8mm x 25.2mm x 22.1mm) and a consistent signal-to-noise ratio of the acquired sEMG (higher than 15 dB). Furthermore, a precise design of the firmware has been performed, handling both signal acquisition and Bluetooth transmission concurrently, thanks to a FreeRTOS custom implementation. In particular, the system adapts to both sEMG and ATC transmission, with an application throughput up to 2 kB s-1 and an average operating time of 80 h (for high resolution sEMG sampling), relaxable to 8Bs-1 throughput and about 230 h operating time (considering a 110mAh battery), in case of ATC acquisition only. Here we share our experience over the years in designing wearable systems for the sEMG detection, specifying in detail how our event-driven approach could benefit the device development phases. Some previous basic knowledge about biosignal acquisition, electronic circuits and programming would certainly ease the repeatability of this tutorial

Post-stroke rehabilitation of hand function based on Electromyography biofeedback

L'abstract è presente nell'allegato / the abstract is in the attachmen

Ubiquitous haptic feedback in human-computer interaction through electrical muscle stimulation

[no abstract

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Hybrid wheelchair controller for handicapped and quadriplegic patients

In this dissertation, a hybrid wheelchair controller for handicapped and quadriplegic patient is proposed. The system has two sub-controllers which are the voice controller and the head tilt controller. The system aims to help quadriplegic, handicapped, elderly and paralyzed patients to control a robotic wheelchair using voice commands and head movements instead of a traditional joystick controller. The multi-input design makes the system more flexible to adapt to the available body signals. The low-cost design is taken into consideration as it allows more patients to use this system

Wireless sensor networks for medical care.

Chen, Xijun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (leaves 72-77).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Design Challenges --- p.2Chapter 1.2 --- Wireless Sensor Network Applications --- p.6Chapter 1.2.1 --- Military Applications --- p.7Chapter 1.2.2 --- Environmental Applications --- p.9Chapter 1.2.3 --- Health Applications --- p.11Chapter 1.3 --- Wireless Biomedical Sensor Networks (WBSN) --- p.12Chapter 1.4 --- Text Organization --- p.13Chapter Chapter 2 --- Design a Wearable Platform for Wireless Biomedical Sensor Networks --- p.15Chapter 2.1 --- Objective --- p.17Chapter 2.2 --- Requirements for Wireless Medical Sensors --- p.19Chapter 2.3 --- Hardware design --- p.21Chapter 2.3.1 --- Materials and Methods --- p.21Chapter 2.3.2 --- Results --- p.24Chapter 2.3.3 --- Conclusion --- p.27Chapter 2.4 --- Software design --- p.28Chapter 2.4.1 --- TinyOS --- p.28Chapter 2.4.2 --- Software Organization --- p.28Chapter Chapter 3 --- Wireless Medical Sensors --- p.32Chapter 3.1 --- Sensing Physiological Information --- p.32Chapter 3.1.1 --- Pulse Oximetry --- p.32Chapter 3.1.2 --- Electrocardiograph --- p.36Chapter 3.1.3 --- Galvanic Skin Response --- p.41Chapter 3.2 --- Location Tracking --- p.43Chapter 3.2.1 --- Outdoor Location Tracking --- p.43Chapter 3.2.2 --- Indoor Location Tracking --- p.44Chapter 3.3 --- Motion Tracking --- p.49Chapter 3.3.1 --- Technology --- p.50Chapter 3.3.2 --- Motion Analysis Sensor Board --- p.51Chapter 3.4 --- Discussions --- p.52Chapter Chapter 4 --- Applications in Medical Care --- p.54Chapter 4.1 --- Introduction --- p.54Chapter 4.2 --- Wearable Wireless Body Area Network --- p.56Chapter 4.2.1 --- Architecture --- p.58Chapter 4.2.2 --- Deployment Scenarios --- p.62Chapter 4.3 --- Application in Ambulatory Setting --- p.63Chapter 4.3.1 --- Method --- p.64Chapter 4.3.2 --- The Software Architecture --- p.66Chapter Chapter 5 --- Conclusions and Future Work --- p.69References --- p.72Appendix --- p.7

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness