Daily Life Monitoring Research Articles

Practicing mindfulness can foster monitoring and acceptance after experiencing negative affect.

Although being mindful, that is, monitoring and accepting one's present feelings, has been shown to be associated with less current negative affect than distraction, acceptance is less endorsed after experiencing negative affect. Thus, acceptance, although effective, may not be endorsed when it is most needed, leading people to endorse other strategies such as distraction instead. In the present study, we examined whether a monitoring and acceptance induction in a laboratory setting (N=258) and a monitoring and acceptance training in daily life (N=125) increased acceptance (Study 1) and monitoring (Study 2) after experiencing negative affect. We found this to be the case: While participants in the control condition showed a significant negative association between prior negative affect and subsequent monitoring and acceptance, participants in the monitor and acceptance condition did not. Thus, even brief monitor and acceptance training is well suited to increasing the likelihood that individuals can monitor and accept their thoughts and feelings when confronted with strong emotions.

Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12–15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Mental fatigue recognition study based on 1D convolutional neural network and short-term ECG signals.

Mental fatigue has become a non-negligible health problem in modern life, as well as one of the important causes of social transportation, production and life accidents. Fatigue detection based on traditional machine learning requires manual and tedious feature extraction and feature selection engineering, which is inefficient, poor in real-time, and the recognition accuracy needs to be improved. In order to recognize daily mental fatigue level more accurately and in real time, this paper proposes a mental fatigue recognition model based on 1D Convolutional Neural Network (1D-CNN), which inputs 1D raw ECG sequences of 5s duration into the model, and can directly output the predicted fatigue level labels. The fatigue dataset was constructed by collecting the ECG signals of 22 subjects at three time periods: 9:00-11:00 a.m., 14:00-16:00 p.m., and 19:00-21:00 p.m., and then inputted into the 19-layer 1D-CNN model constructed in the present study for the classification of mental fatigue in three grades. The results showed that the model was able to recognize the fatigue levels effectively, and its accuracy, precision, recall, and F1 score reached 98.44%, 98.47%, 98.41%, and 98.44%, respectively. This study further improves the accuracy and real-time performance of recognizing multi-level mental fatigue based on electrocardiography, and provides theoretical support for real-time fatigue monitoring in daily life.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Polymeric Conductive Adhesive-Based Ultrathin Epidermal Electrodes for Long-Term Monitoring of Electrophysiological Signals.

Electrophysiology, exploring vital electrical phenomena in living organisms, anticipates broader integration into daily life through wearable devices and epidermal electrodes. However, addressing the challenges of the electrode durability and motion artifacts is essential to enable continuous and long-term biopotential signal monitoring, presenting a hurdle for its seamless implementation in daily life. To address these challenges, an ultrathin polymeric conductive adhesive,poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate)/polyvinyl alcohol/d-sorbitol (PPd) electrode with enhanced adhesion, stretchability, and skin conformability, is presented. The skin conformability and stability of electrodes is designed by theoretical criteria obtained by mechanical analysis.Thus, impedance stability is obtained over 1-week of daily life, and the PPd electrode addresses the challenges related to durability during prolonged usage. Proving stability in electromyography (EMG) signals during high-intensity exercise, the wireless PPd measurement system exhibits high signal-to-noise ratio (SNR) signals even in situations involving significant and repetitive skin deformation. Throughout continuous 1 week-long electrocardiogram (ECG) monitoring in daily life, the system consistently preserves signal quality, underscoring the heightened durability and applicability of the PPd measurement system.

Long-term continuous instrumented insole-based gait analyses in daily life have advantages over longitudinal gait analyses in the lab to monitor healing of tibial fractures.

Introduction: Monitoring changes in gait during rehabilitation allows early detection of complications. Laboratory-based gait analyses proved valuable for longitudinal monitoring of lower leg fracture healing. However, continuous gait data recorded in the daily life may be superior due to a higher temporal resolution and differences in behavior. In this study, ground reaction force-based gait data of instrumented insoles from longitudinal intermittent laboratory assessments were compared to monitoring in daily life. Methods: Straight walking data of patients were collected during clinical visits and in between those visits the instrumented insoles recorded all stepping activities of the patients during daily life. Results: Out of 16 patients, due to technical and compliance issues, only six delivered sufficient datasets of about 12weeks. Stance duration was longer (p = 0.004) and gait was more asymmetric during daily life (asymmetry of maximal force p < 0.001, loading slope p = 0.001, unloading slope p < 0.001, stance duration p < 0.001). Discussion: The differences between the laboratory assessments and the daily-life monitoring could be caused by a different and more diverse behavior during daily life. The daily life gait parameters significantly improved over time with union. One of the patients developed an infected non-union and showed worsening of force-related gait parameters, which was earlier detectable in the continuous daily life gait data compared to the lab data. Therefore, continuous gait monitoring in the daily life has potential to detect healing problems early on. Continuous monitoring with instrumented insoles has advantages once technical and compliance problems are solved.

Accuracy and reliability of wristband smartwatch-based estimation of circadian rhythm of heart rate in stroke patients

Background &amp; Objective: Stroke has a high incidence, disability, and mortality rate, which has a serious impact on the patients’ quality of life. Heart rate (HR) is valuable in the prognostic assessment of stroke patients. Continuous HR tracking helps to detect abnormal indicators and provide interventions timely to avoid disease progression. However, the cuff-based or electrode-based HR monitoring tools commonly used in clinic are not suitable for continuous monitoring in daily life. The aim of this study was to verify the accuracy and reliability of a wristband smartwatch in monitoring HR over 24-hour in stroke patients, including in terms of the circadian rhythm characteristics of HR. Methods: 25 stroke patients underwent 24-hour HR monitoring by wearing a smartwatch and a clinical monitor. We use ambulatory blood pressure monitors and bedside monitors as the gold standard for clinical HR monitoring. The degree of association between the two measurements was compared using Pearson’s correlation coefficient. Bland-Altman analysis showed the accuracy of the smartwatch in HR monitoring. Intraclass correlation coefficients (ICC) and coefficients of variation (CV) were used to verify the reliability of the wristband smartwatch for repeated measurements. Results: A total of 625 heart rate pairs were acquired. The smartwatches had an average error of 0.816 bpm in 24-hour, with no difference from standard monitors (P&gt;0.05), indicating good accuracy. The intraday repeated measures ICC was 0.969 and the interday repeated measures ICC was 0.788, indicating good reliability. Conclusions: The smartwatch is acceptable in detecting 24-hour ambulatory HR in stroke patients, and in the future, long-term HR monitoring with diurnal variations can be performed in daily life.

Artificial intelligence and Internet of Things‐enabled decision support system for the prediction of bacterial stalk root disease in maize crop

AbstractAlthough the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)‐enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI‐enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT‐enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI‐based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI‐ and IoT‐enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real‐time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

Fabrication strategies and microscale sensing functionalities of mechanochromic colloidal photonic crystals for underwater applications

Mechanochromic colloidal photonic crystals (PCs), which typically integrate a self-assembled PC array with highly elastic medium, exhibit the ability to reversibly respond to external mechanical stimuli by altering the periodicity of PC structures. Nowadays, leveraging visible indications and optical signals for mechanical forces, mechanochromic colloidal PCs have been widely used in reflecting body motion, health monitoring, and communications in daily life. However, despite their extensive applications on land, it is vital to explore the potential of mechanochromic sensing applications under water, where message transmission mainly relies on body gestures and motions. This review comprehensively examines recent advancements in mechanochromic colloidal PCs and their underwater applications. The first part introduces the response mechanism of mechanochromic colloidal PCs, emphasizing the main principles that facilitate sensing on the micro scale. The second part describes the fabrication strategies for constructing mechanochromic colloidal PCs, demonstrating various approaches to establish optical sensors with specific functionalities. The final section discusses the confronted challenges and summarizes the potential opportunities in developing mechanochromic colloidal PCs for underwater sensing applications.

Virtual coach and telerehabilitation for Parkinson´s disease patients: vCare system

Abstract Aim The vCare system is a virtual coach that involves physical and cognitive telerehabilitation and a daily life monitoring system. This pilot study aims to evaluate the vCare pilot test for Parkinson’s disease (PD) patients and to analyze the usability and the satisfaction level of patients and their quality of life (QoL). Subject and methods Twenty PD patients were randomized, 10 patients into the vCare group focused on personalized home telerehabilitation [motor and cognitive rehabilitation (4 days/week for 4 months)], while the control group (10 patients) continued the clinical standard at the clinic. A pre-post clinical evaluation and a cost-utility study were performed. Results Repeated measures ANOVA showed significant improvement in the PD vCare group compared to the control group (p&lt;.05). Specifically, the PD vCare group showed significant improvement in cognition (p=.016), and QoL dimensions of mobility (p=.008), self-care (p=.008), daily activities (p=.010) and pain/discomfort (p=.004) at post-treatment. vCare PD patients showed high adherence to the vCare system (90.5-100%). Costs per patient in the control group were higher (€5,108.26) than in the vCare group (€2,243.07). Conclusion The PD vCare group significantly increase their QoL, cognition, motor symptoms, and daily life activities compared to the control group. Patients showed high adherence to the vCare coach, the care plan, rehabilitation, and devices. The vCare system seems to be an optimal and cost-effective tool for telerehabilitation in PD.

Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion.

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor's type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time-frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson's disease severity, surpassing DCLSTM's 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases.

Coverage Optimization Based on Airborne Fog Computing for Internet of Medical Things

The ubiquitous usage of Internet of Medical Things (IoMT) enables real-time health monitoring in daily life. However, a huge amount of generated medical data causes great trouble to IoMT devices, owing to their power-constrained and computation-limited features. On the other hand, unmanned aerial vehicles (UAVs) can achieve a seamless wireless connection to IoMT devices. In this article, we leverage UAVs as the aerial fog nodes to provide computational resources to the IoMT devices. We aim to minimize the number of UAVs for covering the IoMT devices on the ground. This problem is modeled as a discrete nonlinear combinatory optimization problem with NP-hardness. A particle swarm optimization (PSO)-based strategy is put forward to solve this problem. In particular, we introduce greedy heuristic to speed up the searching process. The experimental results show that the proposed algorithms GASM_a and GASM_b can achieve better performance compared to the greedy and random approaches in terms of optimal values. In addition, the enhanced algorithm GASM_b can obtain the better result than GASM_a. Meanwhile, the execution time is also averagely increased by 16.51% compared to GASM_a. Increasingly, low hardware prices enable large-scale deployment of UAVs for IoMT in healthcare systems. Our study shows the advantages in wireless coverage for ground patients by UAVs in terms of efficiency, and cost that will contribute to the development of UAV assisted healthcare systems.

Feasibility, credence, and usefulness of out-of-office cuffless blood pressure monitoring using smartwatch: a population survey

BackgroundCuffless blood pressure (BP) measurement, enabled by recent advances in wearable devices, allows for BP monitoring in daily life. This study aims to evaluate the feasibility, cresdence, and usefulness of cuffless BP monitoring through a population survey.MethodsDuring the "Daily BP Measurement with Your Galaxy Watch" campaign held by the Korean Society of Hypertension, participants were asked to share their experiences with cuffless BP measurement using a smartwatch application through an online survey. The questionnaire included questions about age, underlying medical conditions, smartwatch utilization, experience with BP calibration, the reliability of BP values measured by a smartwatch, and willingness to use the BP monitoring function in the future.ResultsA total of 1071 participants responded to the survey. The largest age group (decile) was 50-59 years old (33.3%), followed by 40-49 years old (29.9%). Although nearly half of the participants (47.5%) had no chronic diseases, 40.1% reported having hypertension. BP monitoring was the most frequently utilized smartwatch function (95.8%), followed by heart rate measurement (87.1%). 31.8% of participants reported that BP values measured by the smartphone application were "very accurate and helpful," while 63.5% rated them as "slightly lower (44.4%)" or "higher (19.1%)" compared to the standard home BP monitoring device. 93% of the participants reported utilizing the BP monitoring function at least once a week. Regarding the BP calibration process, most participants (93.9%) calibrated the BP measurement application themselves, and 50.8% rated the difficulty level as "very easy."ConclusionCuffless BP measurement using a smartwatch application was feasible in the general population, including the self-calibration process. However, the satisfaction level in terms of accuracy is still modest, indicating a need for further development.

Capturing postural blood pressure dynamics with near-infrared spectroscopy-measured cerebral oxygenation

Orthostatic hypotension (OH) is highly prevalent in older adults and associated with dizziness, falls, lower physical and cognitive function, cardiovascular disease, and mortality. OH is currently diagnosed in a clinical setting with single-time point cuff measurements. Continuous blood pressure (BP) devices can measure OH dynamics but cannot be used for daily life monitoring. Near-infrared spectroscopy (NIRS) has potential diagnostic value in measuring cerebral oxygenation continuously over a longer time period, but this needs further validation. This study aimed to compare NIRS-measured (cerebral) oxygenation with continuous BP and transcranial Doppler-measured cerebral blood velocity (CBv) during postural changes. This cross-sectional study included 41 participants between 20 and 88 years old. BP, CBv, and cerebral (long channels) and superficial (short channels) oxygenated hemoglobin (O2Hb) were measured continuously during various postural changes. Pearson correlations between BP, CBv, and O2Hb were calculated over curves and specific characteristics (maximum drop amplitude and recovery). BP and O2Hb only showed good curve-based correlations (0.58–0.75) in the initial 30 s after standing up. Early (30–40 s) and 1-min BP recovery associated significantly with O2Hb, but no consistent associations were found for maximum drop amplitude and late (60–175 s) recovery values. Associations between CBv and O2Hb were poor, but stronger for long-channel than short-channel measurements. BP associated well with NIRS-measured O2Hb in the first 30 s after postural change. Stronger associations for CBv with long-channel O2Hb suggest that long-channel NIRS specifically reflects cerebral blood flow during postural transitions, necessary to better understand the consequences of OH such as intolerance symptoms.

The role of wrist-worn technology in the management of Parkinson's disease in daily life: A narrative review.

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.

Opal Actigraphy (Activity and Sleep) Measures Compared to ActiGraph: A Validation Study.

Physical activity and sleep monitoring in daily life provide vital information to track health status and physical fitness. The aim of this study was to establish concurrent validity for the new Opal Actigraphy solution in relation to the widely used ActiGraph GT9X for measuring physical activity from accelerometry epic counts (sedentary to vigorous levels) and sleep periods in daily life. Twenty participants (age 56 + 22 years) wore two wearable devices on each wrist for 7 days and nights, recording 3-D accelerations at 30 Hz. Bland-Altman plots and intraclass correlation coefficients (ICCs) assessed validity (agreement) and test-retest reliability between ActiGraph and Opal Actigraphy sleep durations and activity levels, as well as between the two different versions of the ActiGraph. ICCs showed excellent reliability for physical activity measures and moderate-to-excellent reliability for sleep measures between Opal versus Actigraph GT9X and between GT3X versus GT9X. Bland-Altman plots and mean absolute percentage error (MAPE) also show a comparable performance (within 10%) between Opal and ActiGraph and between the two ActiGraph monitors across activity and sleep measures. In conclusion, physical activity and sleep measures using Opal Actigraphy demonstrate performance comparable to that of ActiGraph, supporting concurrent validation. Opal Actigraphy can be used to quantify activity and monitor sleep patterns in research and clinical studies.

An extended-gate-type organic transistor-based enzymatic sensor for dopamine detection in human urine

Given the increasing attention paid to health monitoring in daily life, the development of easy-to-handle sensor devices is desirable. Therefore, an appropriate sensor design for highly selective detection of a specific biomarker in human samples is required to obtain accurate analytical results. Hence, we report an extended-gate-type organic field-effect transistor (OFET)-based biosensor functionalized with a laccase-linked mediator for the detection of dopamine in human urine. In the detection mechanism, the oxidation of dopamine by laccase with a mediator (i.e., an N-ethylphenazonium moiety) induces an electron relay on the extended-gate electrode, resulting in a quantitative change in the transistor characteristics. Using an extended-gate electrode functionalized with an enzyme-linked self-assembled monolayer, the OFET selectively detected the target dopamine over the interferents. The limit of detection (i.e., 0.029 ppm, 0.19 μM) estimated by the 3σ method suggests the usability of the proposed OFET-based sensor for the detection of dopamine levels in human urine, taking into consideration of actual concentration of urinary dopamine (< 0.39 ppm, 2.5 μM). Notably, the demonstration of the spike-and-recovery test for non-diluted human urine samples without any pretreatment clarified the feasibility of the OFET-based biosensor for urinalysis, judging by the highly accurate recovery rate (97−104%). We believe that the designed OFET device for urinalysis is a potent sensor platform for accurate monitoring of biomarker levels.

Semantic segmentation-based system for fall detection and post-fall posture classification

Fall is one of the most critical issues faced by elders in their daily life. The consequences of falls range from fatal injuries, severe injuries to no injuries. Therefore, an effective system for detecting falls and treating post-fall injuries is important. Unlike wearable sensors, camera-based systems seem more comfortable and flexible for daily life monitoring. However, monitoring human activities in real settings using cameras is not only challenging but also pose privacy issues. To mitigate this problem, we propose a surveillance camera-based framework for fall detection and post-fall classification where the human silhouette is extracted and used instead of raw images. The human silhouette is obtained using a pixel-level classification based on a Multi-Scale Skip Connection Segmentation Network (MSSkip), which is shown to achieve state-of-the-art performance on the validation set from PASCAL VOC 2012 dataset for the class Person with an IoU of 90%. The temporal and spatial variations of the human poses are fed to a Convolutional Long Short Term Memory (ConvLSTM) network to detect whether or not a fall has occurred. The proposed fall detection method achieved an F1-score of 97.68% on the UP Fall dataset, and state-of-art performance on the UR Fall detection database. For post-fall posture classification, the Xception network is shown to achieve an F1-score of 97.85% on our customized post-fall dataset.

Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment

BackgroundEarly diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.MethodWe employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.ResultsThe highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.ConclusionsThis study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.