<p><a>Preeclampsia leads to increased risk of morbidity and mortality for both mother and fetus. Most previous studies have largely neglected mechanical compression of the left renal vein by the gravid uterus as a potential mechanism. In this study we first used a murine model to investigate the pathophysiology of left renal vein constriction. The results indicate that prolonged renal vein stenosis after 14 days can cause renal necrosis and an increase in blood pressure (BP) of roughly 30 mmHg. The second part of this study aimed to automate a diagnostic tool, known as the supine pressor test (SPT), to enable pregnant women to assess their preeclampsia development risk. A positive SPT has been previously defined as an increase of at least 20 mmHg in diastolic BP when switching between left lateral recumbent and supine positions. The results from this study established a baseline BP increase between the two body positions in non-pregnant female subjects and demonstrated the feasibility and utility of an automated SPT in pregnant women. Our results demonstrate that there is a baseline increase in BP of roughly 10-14 mmHg and that pregnant women can autonomously perform the SPT. Overall, this work in both rodents and humans suggests that 1) stenosis of the left renal vein in mice leads to elevation in BP and acute renal failure, 2) non-pregnant women experience a baseline increase in BP when they shift from left lateral recumbent to supine position, and 3) the SPT can be automated and used autonomously.</a></p> <br> <p> </p>

Next-generation soft robots, wearable health monitoring devices, and human-machine interfaces require electronic systems that can maintain their performance under deformations. Thus, researchers have been developing materials and methods to enable high-performance soft electronic systems in diverse applications. While a variety of solutions have been presented, development of stretchable materials with a combination of high stretchability, electrical conductivity, cyclic stability, and manufacturability is still an open challenge. Throughout this dissertation, gallium-based<br>liquid metal alloy is used as the conductive material, leveraging its high conductivity and intrinsic stretchability for maintained performance under deformations. This dissertation presents both new liquid metal-based conductive materials and scalable manufacturing methods for the development of a diverse range of flexible and stretchable electronic circuits. First, a laser sintering method was developed to coalesce liquid metal micro/nanoparticles into soft, conductive structures enabled by oxide rupturing. The fast, non-contact, and maskless laser sintering technique, in combination with large-area spray-printing deposition, and high-throughput emulsion processing, provided a methodology to create different physical manifestations of liquid metal-based soft, stretchable, and reconfigurable electronics. Second, a liquid metal-based biphasic material was created using a thermal processing technique, yielding a printable, mechanically stable, and extremely stretchable conductor. This material’s compatibility with existing scalable manufacturing methods, robust interfaces with off-the-shelf electronic components, and electrical/mechanical cyclic stability enabled direct conversion of established circuit board assemblies to stretchable forms. The work presented in this dissertation paves the way for future mass-manufacturing of<br>soft, stretchable circuits for direct integration into smart garments or soft robots. <br>

Krisur is a project about a low energy device that works as a “lifesaving” object with the function of pairing, or connecting, the single individual to the Government in case of emergency. The aim of this is to explore and tackle new possibilities for connecting people to a no-hackable source or to a social system (e.g., Hesa Fredrik). This will also bring a reflection around geo-political transformation over time and in what way lo-fi technology is connected to it.

Opioid use disorder (OUD) is one of the most pressing public health problems of our time, with staggering morbidity, social impact, and economic costs. Prescription opioids play a critical role in the opioid crisis as they increase exposure and availability in the general population, making them an attractive target for much needed prevention and risk mitigation strategies. Opioid exposure, including legitimate prescription use, leads to a variety of physiologic adaptations (e.g. dependence) that may be leveraged to understand and identify risk of misuse. Mobile health (mHealth) tools, including wearable sensors have great potential in this space, but have been underutilized. Of specific interest are digital biomarkers, or end-user generated physiologic or behavioral measurements that correlate with events of interest, health, or pathology. Preliminary data support the concept that wearable sensors can detect digital biomarkers of opioid use and may provide clues regarding individual physiologic adaptations to opioid use over time. This dissertation follows a path though the exploration and refinement of these digital biomarkers of opioid use in various clinical use cases. Longitudinal data from individuals treated with opioids for acute pain will be explored through various machine learning models to detect opioid use and to explore patient and treatment factors that impact model performance. Next, a signal processing approach will be undertaken to explore the effects of opioid agonism in a different population of individuals- those presenting with opioid toxicity and precipitated withdrawal. Both approaches will be combined to further refine the digital biomarker capabilities, this time with a focus on the difference between opioid naive and chronic users. And finally, usability, facilitators and barriers to use of a sensor-based monitoring system for opioids will be evaluated through a qualitative lens. Taken together, theses data support the development of a smart technology, driven by empirically derived algorithms which can be used to monitor opioid use, support safe prescribing practices, and reduce OUD and death.

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The medical health industry is entering a new era and technology will play a great role in this area. Equipment in hospitals is in many cases strictly dependent on technology that works. However, technology in the medical health industry will maybe become a bigger part of our private lifestyle. This lifestyle includes digital health apps, wearables and devices that track your daily physical routines with “Internet of things”. These ways of keeping track of your health can be used for private purposes, but also to complement medical studies with clinical results. This thesis will focus on how wearables can complement a medical study where patients with severe heart failure will use the smartwatch Apple Watch. This smartwatch will collect data on patients daily physical activity pattern and thereafter analyze this data in order to find activity patterns. This thesis intends to answer the questions How can wearables such as the Apple Watch benefit medical heart research? and what makes the Apple Watch a suitable wearable for the medical study at Lund’s University Hospital? Interviews were therefore held with medical heart researchers and addressed the purpose of the medical study and their choice of wearable. Thereafter, a examination of the Apple Watch was conducted and it together with the interview indicated that the Apple Watch in fact is a suitable wearable. Finally, an exportation process where data from the Apple Watch was done where the exported data then was decoded in Microsoft Excel. The purpose of this was to examine statements that were revealed in the interview. That being said, the thesis came to the conclusion that the Apple Watch contributes a lot when mixing complementing data from wearables with clinical records. Another conclusion was that this tracking device was suitable for the medical study. In what extension the Apple Watch is suitable, is yet unclear since the medical study is in need of further patients and research where one compares wearables against each other.

<p>Wearable devices and their ubiquitous use and deployment across multiple areas of health provide key insights in patient and individual status via big data through sensor capture at key parts of the individual’s body. While small and low cost, their limitations rest in their computational and battery capacity. One key use of wearables has been in individual activity capture. For accelerometer and gyroscope data, oscillatory patterns exist between daily activities that users may perform. By leveraging spatial and temporal learning via CNN and LSTM layers to capture both the intra and inter-oscillatory patterns that appear during these activities, we deployed data sparsification via autoencoders to extract the key topological properties from the data and transmit via BLE that compressed data to a central device for later decoding and analysis. Several autoencoder designs were developed to determine the principles of system design that compared encoding overhead on the sensor device with signal reconstruction accuracy. By leveraging asymmetric autoencoder design, we were able to offshore much of the computational and power cost of signal reconstruction from the wearable to the central devices, while still providing robust reconstruction accuracy at several compression efficiencies. Via our high-precision Bluetooth voltmeter, the integrated sparsified data transmission configuration was tested for all quantization and compression efficiencies, generating lower power consumption to the setup without data sparsification for all autoencoder configurations. </p> <p><br></p> <p>Human activity recognition (HAR) is a key facet of lifestyle and health monitoring. Effective HAR classification mechanisms and tools can provide healthcare professionals, patients, and individuals key insights into activity levels and behaviors without the intrusive use of human or camera observation. We leverage both spatial and temporal learning mechanisms via CNN and LSTM integrated architectures to derive an optimal classification architecture that provides robust classification performance for raw activity inputs and determine that a LSTMCNN utilizing a stacked-bidirectional LSTM layer provides superior classification performance to the CNNLSTM (also utilizing a stacked-bidirectional LSTM) at all input widths. All inertial data classification frameworks are based off sensor data drawn from wearable devices placed at key sections of the body. With the limitation of wearable devices being a lack of computational and battery power, data compression techniques to limit the quantity of transmitted data and reduce the on-board power consumption have been employed. While this compression methodology has been shown to reduce overall device power consumption, this comes at a cost of more-or-less information loss in the reconstructed signals. By employing an asymmetric autoencoder design and training the LSTMCNN classifier with the reconstructed inputs, we minimized the classification performance degradation due to the wearable signal reconstruction error The classifier is further trained on the autoencoder for several input widths and with quantized and unquantized models. The performance for the classifier trained on reconstructed data ranged between 93.0\% and 86.5\% accuracy dependent on input width and autoencoder quantization, showing promising potential of deep learning with wearable sparsification. </p>

Indiana University-Purdue University Indianapolis (IUPUI) / Smart wearables, known as emerging paradigms for vital big data capturing, have been attracting intensive attentions. However, one crucial problem is their power-hungriness, i.e., the continuous data streaming consumes energy dramatically and requires devices to be frequently charged. Targeting this obstacle, we propose to investigate the biodynamic patterns in the data and design a data-driven approach for intelligent data compression. We leverage Deep Learning (DL), more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data. The minimized energy need, even taking into consideration the CAE-induced overhead, is tremendously lower than the original energy need. Further, compared with state-of-the-art wavelet compression-based method, our method can compress the data with a dramatically lower error for a similar energy budget. Our experiments and the validated approach are expected to boost the energy efficiency of wearables, and thus greatly advance ubiquitous big data applications in era of smart health. In recent years, there has also been a growing interest in edge intelligence for emerging instantaneous big data inference. However, the inference algorithms, especially deep learning, usually require heavy computation requirements, thereby greatly limiting their deployment on the edge. We take special interest in the smart health wearable big data mining and inference. Targeting the deep learning’s high computational complexity and large memory and energy requirements, new approaches are urged to make the deep learning algorithms ultra-efficient for wearable big data analysis. We propose to leverage knowledge distillation to achieve an ultra-efficient edge-deployable deep learning model. More specifically, through transferring the knowledge from a teacher model to the on-edge student model, the soft target distribution of the teacher model can be effectively learned by the student model. Besides, we propose to further introduce adversarial robustness to the student model, by stimulating the student model to correctly identify inputs that have adversarial perturbation. Experiments demonstrate that the knowledge distillation student model has comparable performance to the heavy teacher model but owns a substantially smaller model size. With adversarial learning, the student model has effectively preserved its robustness. In such a way, we have demonstrated the framework with knowledge distillation and adversarial learning can, not only advance ultra-efficient edge inference, but also preserve the robustness facing the perturbed input. / 2023-06-01

The Apple Watch is capable of recording single-lead electrocardiograms (ECGs). To incorporate such devices in routine medical care, the reliability of such devices to obtain precordial leads needs to be validated. The purpose of this study was to assess the feasibility and reliability of a smartwatch (SW) to obtain precordial leads compared to standard ECGs. We included 100 participants (62 male, aged 62.8 ± 13.1 years) with sinus rhythm and recorded a standard 12-lead ECG and the precordial leads with the Apple Watch. The ECGs were quantitively compared. A total of 98 patients were able to record precordial leads without assistance. A strong correlation was observed between the amplitude of the standard and SW-ECGs’ waves, in terms of P waves, QRS-complexes, and T waves (all p-values < 0.01). A significant correlation was observed between the two methods regarding the duration of the ECG waves (all p-values < 0.01). Assessment of polarity showed a significant and a strong concordance between the ECGs’ waves in all six leads (91–100%, all p-values < 0.001). In conclusion, 98% of patients were able to record precordial leads using a SW without assistance. The SW is feasible and reliable for obtaining valid precordial-lead ECG recordings as a validated alternative to a standard ECG.

Some of the recently released smartwatch products feature a single-lead electrocardiogram (ECG) recording capability. The reliability of obtaining 3-lead ECG with smartwatches is yet to be confirmed in a large study. This study aimed to assess the feasibility and reliability of smartwatch to obtain 3-lead ECG recordings, the classical Einthoven ECG leads I-III compared to standard ECG. To record lead I, the watch was worn on the left wrist and the right index finger was placed on the digital crown for 30 s. For lead II, the watch was placed on the lower abdomen and the right index finger was placed on the digital crown for 30 s. For lead III, the same process was repeated with the left index finger. Spearman correlation and Bland-Altman tests were used for data analysis. A total of 300 smartwatch ECG tracings were successfully obtained. ECG waves’ characteristics of all three leads obtained from the smartwatch had a similar duration, amplitude, and polarity compared to standard ECG. The results of this study suggested that the examined smartwatch (Apple Watch Series 4) could obtain 3-lead ECG tracings, including Einthoven leads I, II, and III by placing the smartwatch on the described positions.