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1. “Going Rogue”: Re-coding Resistance with Type 1 Diabetes

   https://doi.org/10.14361/dcs-2018-0208  

   Gottlieb, Samantha D. ; Cluck, Jonathan ( December 2018 , Digital Culture & Society)

   null (Ed.)

   Abstract This paper explores our collaborative STS and anthropological project with type 1 diabetes (T1D) hardware “hacking” communities, whose work focuses on reverse-engineering and extracting data from medical devices such as insulin pumps and continuous glucose monitoring systems (CGMS) to create do-it-yourself artificial pancreas systems (APS). Rather than using these devices within their prescriptive and prescribed purposes (surveillance and treatment monitoring), these “hackers” repurpose, reinterpret, and redirect of the possibilities of medical surveillance data in order to reshape their own treatment. Through “deliberate non-compliance” (Scibilia 2017) with cliniciandeveloped treatment guidelines, T1D device hackers deliberatively engage with clinicians’ conceptions and formulations of what constitutes “good treatment” and empower themselves in discussions about the effectiveness of treatment guidelines. Their non-compliance is, however, neither negligence, as implied by the medical category of patients who fail to comply with clinical orders, nor ignorance, but a productive and creative response to their embodied expertise, living with a chronic and potentially deadly condition. Our interlocutors’ explicit connections with the free and open source software principles suggests the formation of a “recursive public” (Kelty 2008) in diabetes research and care practices, from a patient-centered “medical model” to a diverse and divergent patient-led model. The philosophical and ethical underpinnings of the open source and collaborative strategies these patients draw upon radically reshape the principles that drive the commercial health industry and government regulatory structures. 

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2. Concerns in the Blurred Divisions Between Medical and Consumer Neurotechnology

   https://doi.org/10.1109/JSYST.2020.3032609  

   Paek, Andrew Y. ; Brantley, Justin A. ; Evans, Barbara J. ; Contreras-Vidal, Jose L. ( December 2020 , IEEE Systems Journal)

   null (Ed.)

   Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical uses. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement. 

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3. Deep neural networks for wearable sensor-based activity recognition in Parkinson’s disease: investigating generalizability and model complexity

   https://doi.org/10.1186/s12938-024-01214-2  

   Davidashvilly, Shelly ; Cardei, Maria ; Hssayeni, Murtadha ; Chi, Christopher ; Ghoraani, Behnaz ( February 2024 , BioMedical Engineering OnLine)

   The research gap addressed in this study is the applicability of deep neural network (NN) models on wearable sensor data to recognize different activities performed by patients with Parkinson’s Disease (PwPD) and the generalizability of these models to PwPD using labeled healthy data.

   The experiments were carried out utilizing three datasets containing wearable motion sensor readings on common activities of daily living. The collected readings were from two accelerometer sensors. PAMAP2 and MHEALTH are publicly available datasets collected from 10 and 9 healthy, young subjects, respectively. A private dataset of a similar nature collected from 14 PwPD patients was utilized as well. Deep NN models were implemented with varying levels of complexity to investigate the impact of data augmentation, manual axis reorientation, model complexity, and domain adaptation on activity recognition performance.

   A moderately complex model trained on the augmented PAMAP2 dataset and adapted to the Parkinson domain using domain adaptation achieved the best activity recognition performance with an accuracy of 73.02%, which was significantly higher than the accuracy of 63% reported in previous studies. The model’s F1 score of 49.79% significantly improved compared to the best cross-testing of 33.66% F1 score with only data augmentation and 2.88% F1 score without data augmentation or domain adaptation.

   These findings suggest that deep NN models originating on healthy data have the potential to recognize activities performed by PwPD accurately and that data augmentation and domain adaptation can improve the generalizability of models in the healthy-to-PwPD transfer scenario. The simple/moderately complex architectures tested in this study could generalize better to the PwPD domain when trained on a healthy dataset compared to the most complex architectures used. The findings of this study could contribute to the development of accurate wearable-based activity monitoring solutions for PwPD, improving clinical decision-making and patient outcomes based on patient activity levels.

    

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4. Cloud‐Integrated Smart Nanomembrane Wearables for Remote Wireless Continuous Health Monitoring of Postpartum Women

   https://doi.org/10.1002/advs.202307609  

   Matthews, Jared ; Soltis, Ira ; Villegas‐Downs, Michelle ; Peters, Tara A. ; Fink, Anne M. ; Kim, Jihoon ; Zhou, Lauren ; Romero, Lissette ; McFarlin, Barbara L. ; Yeo, Woon‐Hong ( January 2024 , Advanced Science)

   Noncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long‐term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at‐home cardiovascular monitoring of postpartum women—a group with urgently unmet NCD needs in the United States—using a cloud‐integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real‐time waveform analytics, including medical device‐grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month‐long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision‐making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice.

    

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5. Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs

   https://doi.org/10.1038/s41598-023-37013-3  

   Mehrdad, Sarmad ; Shamout, Farah E. ; Wang, Yao ; Atashzar, S. Farokh ( June 2023 , Scientific Reports)

   Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient’s home. In this study, we develop and compare two prognostic models that predict if a patient will experience deterioration in the forthcoming 3 to 24 h. The models sequentially process routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. These models are also provided with basic patient information, including sex, age, vaccination status, vaccination date, and status of obesity, hypertension, or diabetes. The difference between the two models is the way that the temporal dynamics of the vital signs are processed. Model #1 utilizes a temporally-dilated version of the Long-Short Term Memory model (LSTM) for temporal processes, and Model #2 utilizes a residual temporal convolutional network (TCN) for this purpose. We train and evaluate the models using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA. The convolution-based model outperforms the LSTM based model, achieving a high AUROC of 0.8844–0.9336 for 3 to 24 h deterioration prediction on a held-out test set. We also conduct occlusion experiments to evaluate the importance of each input feature, which reveals the significance of continuously monitoring the variation of the vital signs. Our results show the prospect for accurate deterioration forecast using a minimum feature set that can be relatively easily obtained using wearable devices and self-reported patient information.

    

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