Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring

Use of inertial sensory systems to monitor and detect seizure episodes in patients suffering from epilepsy is investigated via numerical simulations and experiments. Numerical simulations employ a mathematical model that is able to predict human body dynamic responses during a typical epileptic seizure. An optimized inertial sensor placement procedure is developed to address achievement of highest possible sensing resolution in determining angular accelerations with minimal errors. In addition, a joint torque estimation procedure is formulated to assist in the future development of a possible detection scheme. Experimental motion data obtained from an epileptic seizure patient as well as a healthy subject via a cluster of inertial measurement sensors formed a basis for proposing a suitable detection scheme based on non-linear response analysis. In particular, preliminary experimental data analysis has shown that the proposed modified Poincaré Map based scheme can become an effective tool in detecting of seizure via inertial measurements

Identification automatique des crises d'épilepsie : développement d'un système non-invasif

RÉSUMÉ L’épilepsie est une condition neurologique caractérisée par des interruptions non-prévisibles et répétées du fonctionnement normal du cerveau. Présentement, le seul moyen fiable d’arriver à l’identification des crises est à l’aide d’un électroencéphalogramme (EEG). Le suivi à long terme des crises d’épilepsie est cependant difficile par EEG en dehors du milieu hospitalier. Des vêtements intelligents utilisant des capteurs non-invasifs pourraient permettre d’identifier les crises sans avoir recours à l’EEG. En ce sens, un système de cinq appareils sans-fils a été développé afin de permettre un suivi multimodal des signaux physiologiques. L’échange de données est réalisée par Bluetooth Low Energy. Un Raspberry Pi synchronise et enregistre les données en temps réel. Le système comprend un électrocardiogramme mesurant l’activité électrique du coeur et l’impédance pulmonaire, un oxymètre de pouls et des accéléromètres positionnés au niveau des poignets, des chevilles et du tronc. Quatre systèmes ont été livrés au Centre hospitalier de l’Université de Montréal pour l’acquisition de données sur des patients épileptiques. Un algorithme d’apprentissage machine a ensuite été développé afin d’identifier automatiquement les crises. Une sensibilité de 94.7% et une spécificité de 94.1% ont été obtenues sur des données contenant des crises simulées et une crise réelle. Le système sans-fil a aussi démontré une performance suffisante au niveau de l’exactitude des mesures physiologiques et de l’autonomie pour permettre des acquisitions de longue durée chez les patients épileptiques. Ces résultats préliminaires montrent la possibilité d’identifier automatiquement les crises de manière non-invasive à domicile. Une telle technologie pourrait permettre de mesurer l’efficacité à long terme de médicaments pour traiter l’épilepsie. Elle pourrait aussi améliorer la protection des patients en émettant des alertes en cas de crise.----------ABSTRACT Epilepsy is a neurological condition characterized by unpredictable and repeated interruptions of brain function. Currently, the only reliable way to identify seizures is using an electroencephalogram (EEG). Long-term monitoring of epileptic seizures by EEG is however difficult outside the hospital setting. Smart clothing using non-invasive sensors could help identify seizures without resorting to EEG. In this sense, a system of five wireless devices has been developed to allow multimodal monitoring of physiological signals. Data exchange is carried out by Bluetooth Low Energy. The data is synchronized and recorded in real-time on a Raspberry Pi. The system includes an electrocardiogram measuring electrical activity of the heart and pulmonary impedance, a pulse oximeter and accelerometers at the wrists, ankles and trunk. Four systems were delivered to the Centre hospitalier de l’Université de Montréal for data acquisition on epileptic patients. A machine learning algorithm was then developed to automatically identify seizures. A sensitivity of 94.7% and a specificity of 94.1% were obtained on data containing simulated seizures and one real seizure. The wireless system has also demonstrated sufficient performance in terms of physiological measurements accuracy and autonomy to allow long-term acquisitions in epileptic patients. These preliminary results show the possibility of automatically identifying seizures at home in a non-invasive way. Such technology could measure the long-term effiectiveness of drugs used to treat epilepsy. It could also improve patient protection by issuing alerts when seizures occur

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Wearable sensor technologies applied for post-stroke rehabilitation

Stroke is a common cerebrovascular disease that is recognized as one of the leading causes of death and ongoing disability around the globe. Stroke can lead to losses of various body functions depending on the affected area of the brain and leave significant impacts to the victim’s daily life. Post-stroke rehabilitation plays an important role in improving the life quality of stroke survivors. Properly designed rehabilitation training programs can not only prevent further functional deterioration, but also helps patients gradually regain their body functionalities. However, the delivery of rehabilitation service can be a complex and labour intensive task. In conventional rehabilitation systems, the chart-based ordinal scales are considered the dominant tools for impairment assessment and the administration of the scales primarily relies on the doctor’s manual observation. Measuring instruments such as strain gauge and force platforms can sometimes be used to collect quantitative evidence for some of the body functions such as grip strength and balance. However, the evaluation of the patients’ impairment level using ordinal scales still depend on the human interpretation of the data which can be both subjective and inefficient. The preferred scale and evaluation standard also vary among institutions across different regions which make the comparison of data difficult and sometimes unreliable. Furthermore, the intensive manual supervision and support required in rehabilitation training session limits the accessibility of the service as the regular visit to qualified hospital can be onerous for many patients and the associated cost can impose an enormous financial burden on both the government and the households. The situation can be even more challenging in developing countries due to higher growing rate of stroke population and more limited medical resources. The works presented in this thesis are focused on exploring the possibilities of integrating wearable sensor and pattern recognition techniques to improve the efficiency and the effectiveness of post-stroke rehabilitation by addressing the abovementioned issues. The study was initiated by a comprehensive literature review on the latest motion tracking technologies and non-visual based Inertia Measurement Unit (IMU) had been selected as the most suitable candidate for motion sensing in unsupervised training environment due to its low-cost and easy-to-operate characteristics. Following the design and construction of the 6-axis IMU based Body Area Network (BAN), a series of stroke patient motion data collection experiments had been conducted in conjunction with the Jiaxing 2nd Hospital Rehabilitation Centre in Zhejiang province, China. The collected motion samples were then investigated using various signal processing algorithms and pattern recognition techniques to achieve the three major objectives: automatic impairment level classification for reducing human effort involved in regular clinical assessment, single-index based limb mobility evaluation for providing objective evidence to support unified body function assessment standards, and training motion classification for enabling home or community based rehabilitation training with reduced supervision. At last, the study has been further expanded by incorporating surface Electromyography (sEMG) signal sampled during rehabilitation exercises as an alternative input to enhance accurate impairment level classification. The outcome of the investigations demonstrate that the wearable technology can play an important role within a tele-rehabilitation system by providing objective, accurate and often realtime indications of the recovery process as well as the assistance for training management

Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability

Postural Instability (PI) is a core feature of Parkinson’s Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method. To evaluate gait performance, spatial-temporal (S-T) gait parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy

Optimizing AI at the Edge: from network topology design to MCU deployment

The first topic analyzed in the thesis will be Neural Architecture Search (NAS). I will focus on two different tools that I developed, one to optimize the architecture of Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing that has recently emerged, and one to optimize the data precision of tensors inside CNNs. The first NAS proposed explicitly targets the optimization of the most peculiar architectural parameters of TCNs, namely dilation, receptive field, and the number of features in each layer. Note that this is the first NAS that explicitly targets these networks. The second NAS proposed instead focuses on finding the most efficient data format for a target CNN, with the granularity of the layer filter. Note that applying these two NASes in sequence allows an "application designer" to minimize the structure of the neural network employed, minimizing the number of operations or the memory usage of the network. After that, the second topic described is the optimization of neural network deployment on edge devices. Importantly, exploiting edge platforms' scarce resources is critical for NN efficient execution on MCUs. To do so, I will introduce DORY (Deployment Oriented to memoRY) -- an automatic tool to deploy CNNs on low-cost MCUs. DORY, in different steps, can manage different levels of memory inside the MCU automatically, offload the computation workload (i.e., the different layers of a neural network) to dedicated hardware accelerators, and automatically generates ANSI C code that orchestrates off- and on-chip transfers with the computation phases. On top of this, I will introduce two optimized computation libraries that DORY can exploit to deploy TCNs and Transformers on edge efficiently. I conclude the thesis with two different applications on bio-signal analysis, i.e., heart rate tracking and sEMG-based gesture recognition