Abstract
Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson's disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS's efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient's hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at SAo JoAo Hospital in Portugal.

Abstract
The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors.

Abstract
Wearable devices have been showing promising results in a large range of applications: since industry, to entertainment and, in particular, healthcare. In the scope of movement disorders, wearable devices are being widely implemented for motor symptoms objective assessment. Currently, clinicians evaluate patients' motor symptoms resorting to subjective scales and visual perception, such as in Parkinson's Disease. The possibility to make use of wearable devices to quantify this disorder motor symptoms would bring an accurate follow-up on the disease progression, leading to more efficient treatments. Here we present a novel textile embedded low-power wearable device capable to be used in any scenario of movement disorders assessment due to its seamless, comfort and versatility. Regarding our research, it has already improved the setup of a wrist rigidity quantification system for Parkinson's Disease patients: the iHandU system. The wearable comprises a hardware sensing unit integrated in a textile band with an innovative design assuring higher comfort and easiness-to-use in movement disorders assessment. It enables to collect inertial data (9-axis) and has the possibility to integrate two analog sensors. A web platform was developed for data reading, visualization and recording. To ensure inertial data reliability, validation tests for the accelerometer and gyroscope sensors were conducted by comparison with its theoretical behavior, obtaining very good results.

Abstract
Cathodal transcranial direct current stimulation (c-tDCS) is a non-invasive option for treatment of refractory epilepsy. However, it is still unknown whether this therapy has a positive stabilizing effect on the vegetative function of these patients. Heart Rate Variability (HRV) is considered an efficient tool to monitor the cardiac autonomic system, which has been correlated with the risk of Sudden Unexpected Death in Epilepsy (SUDEP). In this study, changes in HRV are investigated after c-tDCS of six patients (34.50 +/- 11.10 years) with refractory epilepsy, which have been selected at the University Hospital, LMU Munich. Patients were categorized as responders (n=2), non-responders (n=3) and uncategorized (n=1). We analyzed 24 hours of electrophysiological data recorded before and after treatment, and computed HRV metrics (AVNN, SDNN, RMSD, pNN20, pNN50, LH/HF, 0V, 1V, 2LV, 2UV, SD1 and SD2). All patients revealed a change in almost all HRV metrics post stimulation. Grouped all patients, there was a significant (p<0.05) change in RMSSD, pNN50, SD1 and LH/HF. For responders there was an increase in all time domain and nonlinear metrics, which was not seen for non-responders. These results suggest that tDCS exerts significant changes in cardiovascular autonomic system in patients with refractory epilepsy. HRV metrics may also serve as biomarkers of the response to tDCS stimulation. A larger dataset is being gathered for further analysis.

Abstract
Occupational stress is a complex process affecting health and performance. Air Traffic Control is a complex and demanding profession. The current study demonstrates the concept of using a biomonitoring wearable platform (BWP), that combines self-report measures with biomarkers, to track stress among Air Traffic Controllers. A wearable ECG device was used to gather continuously medical-grade ECG data along with a mobile app for daily stress perception, symptoms and events annotation. A total of 256 hours of data from 32 routine work shifts and 5 days-off, from 5 ATCs was recorded with 35 tagged events using Heart Rate Variability metrics- AVNN, RMSSD, pNN50 and LF/HF were computed from ECG data and analyzed during a) shifts vs days off; b) events vs non-events and c) before and after working pauses. ATCs showed low levels of chronic stress using self-reports. Results showed that stress symptomatology slightly increase from the beginning to the end of the shift (Md=1 to Md=2; p<0.05). Statistical significant physiological changes were found between shifts and days off for AVNN and LF/HF (p<0.05), showing higher physiological activation during shifts. A significant reduction of physiological arousal was verified after working pauses, particularly for AVNN and LF/IIF (p<0.001). Self-reported data also suggests the same trend (p<0.005). Findings reinforced the discriminatory power of AVNN and LF/HF for short-term stress classification using HRV measurements. Results suggest that the rotating working system, with pause/resting periods included, effective acted as a recovery period.

Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that impairs people's mobility. Due to its erratic nature and complexity, the progression of the disease differs from person to person, making it difficult to keep track of the patient's progress. These factors, together with the limited number of annual clinical appointments, create the need to have a tool that can help patients and healthcare professionals better manage Parkinson's outside of the clinical environment. PDapp strives to address this need combining mHealth features with the capabilities of the iHandU appcessory, a novel and seamless wearable device designed to measure wrist rigidity, bradykinesia (slow movement), and tremor, thus enabling continuous effective follow-up, while connecting patients and clinicians remotely. The PDapp system is comprised of a mobile application where patients can manage their medication, self-perform various symptom tests, and maintain clinicians informed of relevant events; a specialized web dashboard for clinicians to monitor all their patient's history and recent events; and a cloud database that exhibits existing data in real-time. The first prototype integrates all these components and provides a promising proof-of-concept that, with a few additions, can be a system that brings value to Parkinson's management. This application design and functionalities were developed jointly with clinicians, addressing their problems and needs. The collected feedback was very positive stating that its usability and simplicity is completely suitable for patients to use. PDapp will introduce a complete and innovative methodology to follow-up PD patient's disease progression and support clinicians during appointments and patients at home, guiding medication adjustment for better disease management. This system is intended as one more step to the PD mHealth ecosystem, improving follow-up and disease therapy yet reducing clinicians' workload.