Parkinson’s Disease (PD) symptoms vary widely, making objective assessment challenging. PragmaClin Research Inc. developed the Parkinson's Remote Interactive Monitoring system (PRIMS), which collects the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) motor examination data via an instruction screen and Microsoft Kinect Depth cameras, and assigns severity ratings using machine-learning algorithms. We captured the experiences of people with PD (PwPD) trialing the system. PwPD were recruited via local PD, neurology and geriatric clinics and PD social/support groups. Participants completed the PRIMS trial (performing actions in front of the camera system), a post-assessment survey (including the System Usability Scale (SUS)), and an optional audio-recorded interview. Survey data were analysed descriptively, with interview findings providing additional context. Twenty-seven participants completed the PRIMS trial and survey; 13 completed an optional interview. Most participants were aged 65-69 (44.4%) or 75-79 (33.3%), and male (66.7%), across Hoehn & Yahr stages 1-4. Almost all (95.6%) users reported being ‘extremely’ or ‘somewhat’ satisfied with the assessment, considering PRIMS potentially valuable for symptom monitoring over time, where video-based assessments could complement in-person consultations and communication with healthcare providers. SUS scores (80–85+) reflected excellent usability, with strong agreement on ease-of-use and low perceived-complexity. However, 52.1% 'somewhat' or 'strongly' disagreed that PRIMS could replace face-to-face consultations, noting usability may depend on technological ability, and some questioned whether clinicians would "trust it". Suggestions for improvement included clarified movement demonstrations and addressing participants’ varied perspectives on viewing themselves on camera. Participants considered PRIMS could be available in GP surgeries or health centers, but that home-based (laptop/phone) assessment would be most accessible. PwPD suggest that remote video-based symptom assessment such as PRIMS would be acceptable and usable, aiding communication with healthcare teams on symptom variability, but this must consider technological abilities and setting convenience.

BackgroundWearable devices have the potential to provide reliable and objective assessment and monitoring of Parkinson disease (PD). During design, there is often a focus on technical performance, accuracy, and reliability, with less emphasis on the user experience. ObjectiveThis study explored the user experience of a novel prototype wearable device (wrist- and ankle-worn) to record limb movements (accelerations and angular velocities), and physiological data (eg, photoplethysmography and heart rate information for estimation of blood pressure). MethodsThis qualitative study used internet-based semistructured interviews with people with PD, following wearing prototype devices for 24 hours at home. Interviews were audio-recorded and transcripts analyzed using a hybrid deductive and inductive approach. ResultsSix people with PD, 3 male and 3 female, aged 52-83 years, with mild-to-moderate PD (Hoehn and Yahr scale score ≤3; median MDS-UPDRS [Movement Disorder Society - Unified Parkinson’s Disease Rating Scale] score of 72/199) and cognition within normal limits (6-item Cognitive Impairment Test median score 0), with an average disease duration of 8 years took part in the study. Participants were overall positive toward the device, finding it generally comfortable, light in weight and noninvasive. Five out of 6 participants reported minor problems related to strap adjustability and challenges specific to PD. The prototype was comfortable, but this was a lesser priority than robustness, the device not hindering their usual clothing choices (device size or outward projection, or both), and adjustability for fit, including the need to switch to an elasticated strap. In particular, a discreet design was important, as some individuals may feel self-conscious about wearing visible condition-specific products, due to stigma. The ankle-worn device was perceived as unfamiliar and nondiscreet, with some participants likening it to a prisoner tracking system and dressing specifically to conceal it. The wrist-worn device was considered more user-friendly, especially if designed discreetly and resembling more familiar devices. Five of the 6 participants believed their health care teams should have access to the data, particularly relating to their symptoms, fluctuations, and medication. Three also wished to access these data for self-management. One participant was hesitant regarding the potential benefits of technology to support PD management, preferring to use feedback data personally and relying on their health care team’s usual assessment to guide decisions. Across the group, desirable device technical features included symptom prediction, reminder prompts, and support for medication management. Despite concerns about stigma, most participants were willing to wear PD-specific devices, believing that they could aid better symptom management. ConclusionsIn summary, wearable devices must be discreet, robust, comfortable, and easily applied to promote adherence regardless of technical specifications.

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings. The performance of the same models when evaluated on data from subjects performing unscripted activities unsupervised in their own homes achieved a balanced accuracy of 63%, 63%, and 67%, respectively, in comparison to self-assessment patient diaries, further highlighting their limitations. The ankle-worn sensor was found to be advantageous for the detection of dyskinesias but did not show an added benefit for tremor and bradykinesia detection here.

Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

To test Wireless Implantable Medical Devices (WIMD), we need test benches that replicate the human body as closely as possible, called phantoms. The phantoms are used to get performance results prior to animal testing. For the phantom to replicate as close as possible the body part where the medical device will be implanted, the phantom needs to be accurate in terms of anatomy and dielectric properties. We have been developing a medical device which intends to measure the blood pressure directly within the pulmonary artery. This medical device aims to wirelessly transfer the data from a pressure sensor to outside the body. A torso phantom was previously prototyped, using 3D printed organs filled with liquid replicating the dielectric properties of the different biological layers at the frequency of interest (M. Pigeon, B. O'Flynn, J. Barton and P. O'Sullivan, “3D printed torso phantom for UHF WIMD measurements,” 2021 IEEE APS/URSI, Singapore, Singapore, 2021, pp. 361–362).

Medication adjustments in Parkinson's disease (PD) are driven by patient subjective report and clinicians' rating of motor feature severity (such as bradykinesia and tremor). Objective: As patients may be seen by different clinicians at different visits, this study aims to determine the interrater reliability of upper limb motor function assessment among clinicians treating people with PD (PwPD). Methods: PwPD performed six standardised hand movements from the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), while two cameras simultaneously recorded. Eight clinicians independently rated tremor and bradykinesia severity using a visual analogue scale. We compared intraclass correlation coefficient (ICC) before and after a training/calibration session where high-variance participant videos were reviewed and MDS-UPDRS instructions discussed. Results: In the first round, poor agreement was observed for most hand movements, with best agreement for resting tremor (ICC 0.66 bilaterally; right hand 95 % CI 0.50-0.82; left hand: 0.50-0.81). Postural tremor (left hand) had poor agreement (ICC 0.14; 95% CI 0.04-0.33), as did wrist pronation-supination (right hand ICC 0.34; 95 % CI 0.19-0.56). In post-training rating exercises, agreements improved, especially for the right hand. Best agreement was observed for hand open-close ratings in the left hand (ICC 0.82, 95 % CI 0.64-0.94) and resting tremor in the right hand (ICC 0.92, 95 % CI 0.83-0.98). Discrimination between right and left hand features by raters also improved, except in resting tremor (disimprovement) and wrist pronation-supination (no change). Conclusions: Clinicians vary in rating video-recorded PD upper limb motor features, especially bradykinesia, but this can be improved somewhat with training.

This paper presents a miniaturised magnetic antenna that can be used for Wireless Implanted Medical device (WIMD). This antenna is designed for data transmission. The miniaturisation factor achieved is 1/9 th of the size of a loop antenna in free space. The antenna was designed to work at Ultra High Frequency (UHF) frequencies range with an UHF Radio Frequency Identification (RFID) chip. This design could be used for other frequency ranges. Due to the high miniaturization factor the performances of the antenna are impacted but a read range of up to 80mm was achieved within a simplified phantom representing blood and fat.

Parkinson’s disease is a degenerative neurological disorder that impairs motor functions and is accompanied by a wide range of non-motor symptoms, such as sleep problems. Parkinsonism is assessed during clinical evaluations and via self-administered diaries and, based on these, the required medication therapies are provided to lessen symptoms. Tri-axial accelerometers and gyroscopes have the potential utility to objectively assess the patient’s condition and aid clinicians in their decision-making. People with Parkinson’s often have significant abnormalities in blood pressure due to comorbid age-related cardiovascular disease and orthostatic hypotension, which result in blurred vision, dizziness, and falls. Frequent blood pressure monitoring may aid in the evaluation of such events and differentiate Parkinson’s disease symptoms from those originated by hypotension. In the present paper, a novel technology for the remote monitoring of Parkinsonian symptoms is presented: the WESAA system. It consists of two devices, worn on the wrist and ankle; its main function is to record accelerations and angular velocities from these body parts, together with photoplethysmograph and electrocardiogram data. This information can be elaborated offline to measure common Parkinson’s disease motor symptoms (e.g., tremor, bradykinesia, and dyskinesia), as well as gait speed, sleep-wake cycles, and cuff-less blood pressure measurements. The overall system requirements, market overview, industrial design and ergonomics, system development, user experience, early results of the gathered inertial raw data, and validation of the photoplethysmograph and electrocardiogram signal waveforms are all thoroughly discussed. The developed technology satisfies all system requirements, and the sensors adopted provided outcomes comparable with gold standard techniques.

Background The increased use of wearable sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the older adult population in remote and rural areas, who may struggle with long commutes to clinics. However, the usability of such systems often discourages patients from adopting these services. Objective This study aimed to understand the usability factors that most influence whether an older adult will decide to continue using a wearable device. Methods Older adults across 4 different regions (Northern Ireland, Ireland, Sweden, and Finland) wore an activity tracker for 7 days under a free-living environment protocol. In total, 4 surveys were administered, and biometrics were measured by the researchers before the trial began. At the end of the trial period, the researchers administered 2 further surveys to gain insights into the perceived usability of the wearable device. These were the standardized System Usability Scale (SUS) and a custom usability questionnaire designed by the research team. Statistical analyses were performed to identify the key factors that affect participants’ intention to continue using the wearable device in the future. Machine learning classifiers were used to provide an early prediction of the intention to continue using the wearable device. Results The study was conducted with older adult volunteers (N=65; mean age 70.52, SD 5.65 years) wearing a Xiaomi Mi Band 3 activity tracker for 7 days in a free-living environment. The results from the SUS survey showed no notable difference in perceived system usability regardless of region, sex, or age, eliminating the notion that usability perception differs based on geographical location, sex, or deviation in participants’ age. There was also no statistically significant difference in SUS score between participants who had previously owned a wearable device and those who wore 1 or 2 devices during the trial. The bespoke usability questionnaire determined that the 2 most important factors that influenced an intention to continue device use in an older adult cohort were device comfort (τ=0.34) and whether the device was fit for purpose (τ=0.34). A computational model providing an early identifier of intention to continue device use was developed using these 2 features. Random forest classifiers were shown to provide the highest predictive performance (80% accuracy). After including the top 8 ranked questions from the bespoke questionnaire as features of our model, the accuracy increased to 88%. Conclusions This study concludes that comfort and accuracy are the 2 main influencing factors in sustaining wearable device use. This study suggests that the reported factors influencing usability are transferable to other wearable sensor systems. Future work will aim to test this hypothesis using the same methodology on a cohort using other wearable technologies.

Parkinson disease (PD), a well-known illness of motor dysfunction, is characterized by a high prevalence of sleep problems due to degenerative brain changes or comorbid conditions [1]. Wearable devices, in the form of actigraphy, have been shown to also be appropriate for monitoring sleep variables in PD patients [2,3] despite reports that current actigraphy algorithms may misinterpret dysfunctional motor activity, such as tremors, bradykinesia, dyskinesia, and limited arm movement while walking, as well as drug-induced hypermotility, thus making their use problematic in people with PD (PwPD) [4]. The ActiGraph GT3X (Pensacola, FL, USA) accelerometer is capable of recording accelerometry measurements for multiple days at 100 Hz, and has been adopted for massive population-level data collections [5]. In the last few years, Van Hees et al. have developed and made freely available open-source software to estimate sleep variables using data collected from similar off-the-shelf wearable inertial sensors [6]. The goal of this study is to investigate if the ActiGraph data, in combination with Van Hees et al.’s heuristic algorithm Distribution of Change in Z-Angle (HDCZA), can correctly estimate sleep variables in PD patients. To the best of the authors’ knowledge, it is the first study that adopts ActiGraph sensors and this methodology for sleep analysis in PwPD. For further comparison, a custom hardware prototype device named WESAA (Wearable Enabled Symptom Assessment Algorithms) developed at the Tyndall National Institute [8] and with the same capabilities as an ActiGraph device was adopted for additional analysis. Nineteen PD subjects took part in a data collection where participants wore the ActiGraph on their most affected wrist for a minimum of 24 hours and simultaneously filled out a sleep diary. Accelerometer data was collected at 100 Hz. Additionally, six subjects repeated the same data collection protocol while wearing the WESAA system. The heuristic algorithm described in [7] was implemented to detect periods of sleep and compared against the participant diaries. Results are shown in Table I and Figure I in the picture below. Accuracy reported on the subjects using the Actigraph was appropriate with an average 77.8±13.6%, even though results were quite variable across patients (between 31.6% and 91.2%). Less variability is shown with the WESAA device, even though only 6 subjects have carried out this data collection, with an average accuracy of 81.9±6.2% (71.8%-90.2%).Download : Download high-res image (157KB)Download : Download full-size image The present investigation shows that ActiGraph accelerometry data collected over 24 hours, in conjunction with the heuristic algorithm HDCZA for the detection of sleep periods, is an appropriate approach to estimate sleep duration even in PwPD. The same algorithm adopted on the WESAA hardware device shows even more promising results but further investigations with a larger sample size are required to confirm this. Funding: This work was supported in part by Enterprise Ireland (EI) and Abbvie, Inc. under agreement IP 2017 0625; and in part by the Science Foundation Ireland which are Co-Funded through the European Regional Development Fund under Grant 12/RC/2289-P2-INSIGHT..

Parkinson's disease is often considered as a “movement disorder”. Nevertheless, People with Parkinson's (PwPD) may also experience other non-motor symptoms, such as abnormalities and alterations in blood pressure (BP). Real-time cuffless BP monitoring may help identify the end-users' overall health state and support clinical decision-making, however most of the studies in the field did not investigate PwPD as a possible cohort of reference. To the best of the authors' knowledge, there are no scientific publications, or commercially available wearable solutions, monitoring BP measurements by means of electrocardiogram (ECG) and/or photoplethysmogram (PPG) in a Parkinsonian cohort. Based on a Random Forest (RF) model and a combination of publicly available data and data collected in lab-settings using a wearable prototype developed at Tyndall National Institute, good performance were achieved on both systolic and diastolic BP measurements with a mean absolute error equal to $7.84\pm 8.12$ and $7.51 \pm 6.16\ \text{mmHg}$ , respectively, for PwPD. As BP disorders are highly prevalent in PwPD, and can mimic dopaminergic deficiency, this work is a step forward in the effective management of motor and non-motor symptoms in this complex cohort of patients.

Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson's) [1]. Gait speed is thus a well-known indicator for assessing an individual's functional mobility [2]. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU) [3]. For the present investigation, we evaluated various supervised ML models to estimate gait speed based on IMU data collected on the most dominant lower limb. The Gait Events Data Set by Miraldo et al. [4] was used for the analysis. This dataset consists of metadata and inertial measurements (tri-axial accelerometer and gyroscope @6.75ms/sample) from sensors fixed to the flat part of the tibia on the dominant side, collected from 22 healthy subjects. Each subject walked barefoot six times at three randomly ordered, self-paced speeds (comfortable, slow, fast) on a 40-m long 2-m wide walkway, without curves, with a flat and rigid surface. The trials lasted 30-to-60 seconds. Overall, the dataset contains 9,661 gait strides. The IMU data for each trial was segmented into strides by the method proposed in [5]. Several features (Table 1) were extracted from each stride [6] and then averaged over the length of the trial to be used as input to several multiple linear regression and tree-based models along with participant anthropometrics. The number of trials were split 80%/20% into training and test sets with data from the same subject appearing only in either one or the other. A grid-search was performed on the training set using leave-one-subject-out cross-validation (LOSO-CV) to find the best performing model and mean absolute percentage error (MAPE) reported as the validation metric. The selected model was compared to a dummy regression model used as a baseline. Tables 2-3 show the results for the different models. Ridge regression showed the best results with MAE equal to 0.12 m/s and MAPE to 9.13%, almost three times lower compared to baseline. Fig. 1 shows the difference between ground truth and predicted speed across different subjects, the related Bland-Altman plot, and a sample of the obtained error for one trial. Results obtained are comparable to results in [5,6]. Download : Download high-res image (674KB)Download : Download full-size image This investigation shows that Ridge regression using features extracted from detected gait events in IMU data and participant anthropometrics is an appropriate approach to estimate gait speed, outperforming several other linear and tree-based regression models. The final model achieved promising results with a considerably lower error than the baseline, although further investigation is needed to confirm efficacy at atypical gait speeds, i.e., with populations with movement disorders. Funding: This work was supported in part by Enterprise Ireland (EI) and Abbvie, Inc. under agreement IP 2017 0625; and in part by the Science Foundation Ireland which are Co-Funded through the European Regional Development Fund under Grant 12/RC/2289-P2-INSIGHT.

Background Wearable devices can diagnose, monitor, and manage neurological disorders such as Parkinson disease. With a growing number of wearable devices, it is no longer a case of whether a wearable device can measure Parkinson disease motor symptoms, but rather which features suit the user. Concurrent with continued device development, it is important to generate insights on the nuanced needs of the user in the modern era of wearable device capabilities. Objective This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management.

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based ex-periments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.

The definition of a sensor monitoring strategy is based on the location for water monitoring, sensor performances, data storage and transmission. For any new sensor, available instruments currently used in oceanographic studies are identified to perform comparisons. Suitable transmission technology is selected according to the test conditions: open sea, coastal areas, remote locations, etc. Sensitivity and stress tests are designed to establish confidence limits under different environmental situations, so that the results obtained in planned testing exercises are enabled to certify the performance of the new instruments. In this paper, we will address three key phases to test and certify the performance of new sensors: (1) RD basis for cost-effective sensor development, (2) sensor development, sensor web platform and integration, and (3) field testing

UNSTRUCTURED The increased use of sensor technology has highlighted the potential for remote telehealth services such as rehabilitation. Telehealth services incorporating wearable sensors are most likely to appeal to the elderly population in remote and rural areas who may struggle with lengthy commutes to clinics. However, the usability of such systems can often discourage patients from adopting these services. To increase the adoption rates of wearable technology, the usability factors influencing continued device usage in the elderly population are examined. This work focuses on evaluating the usability of a popular activity tracker, the Xiaomi Mi Band 3, amongst the over 65 population. The study consisted of 65 elders wearing the wearable sensor for 7 days while only doffing the device for charging. The study was conducted across 4 different regions; Northern Ireland, Ireland, Sweden, and Finland, to diminish any geographical differences in usability perception. At the end of the week, a customised usability questionnaire was taken by the participants to gain insights into their experience. The aim of the study was to identify influencing factors on whether an elder would continue using a wearable device. This paper concludes that comfort and accuracy are the two main influencing factors in sustaining wearable device usage. The study formed part of The Smart sENsor Devices fOr rehabilitation and Connected health (SENDoc) project, which assessed the usability of sensors for remote rehabilitation of elders in the Northern Periphery of Europe.

Over the last few decades, a large number of pH sensitive materials with new compositions and structures have been proposed. Solid state sensors based on organic, inorganic and composite materials are actively investigated, with an increasing interest in the performance offered by nano-scale materials. Our review provides a thorough, up-to-date knowledge of a wide range of pH measurement methods and related sensing materials, firstly by introducing well established materials and methods for pH sensing and then, by covering recent developments in inorganic, organic and nano-engineered devices. The main sensor parameters, including sensitivity, stability, response time and testing conditions are reported. Given the importance of pH sensing in environmental applications, in particular seawater monitoring, sensors tested in seawater are highlighted and discussed.

This Special Issue captures a significant portion of the current sensors research excellence in Ireland [...]

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings.

This paper describes the manufacturing of a torso phantom used for radio frequency (RF) measurements of a Wireless Implanted Medical Device (WIMD) implanted in the pulmonary artery. This devices work in the UHF (Ultra High Frequency) band. This phantom has a detailed internal anatomy, with 3D printed organs, to achieve a better approximation than homogeneous phantoms found commercially.