Epileptic seizure forecasting with wearable-based nocturnal sleep features.

OBJECTIVE: Non-invasive biomarkers have recently shown promise for seizure forecasting in people with epilepsy. In this work, we developed a seizure-day forecasting algorithm based on nocturnal sleep features acquired using a smart shirt. METHODS: Seventy-eight individuals with epilepsy admitted to the Centre hospitalier de l'Université de Montréal epilepsy monitoring unit wore the Hexoskin biometric smart shirt during their stay. The shirt continuously measures electrocardiography, respiratory, and accelerometry activity. Ten sleep features, including sleep efficiency, sleep latency, sleep duration, time spent in non-rapid eye movement sleep (NREM) and rapid eye movement sleep (REM), wakefulness after sleep onset, average heart and breathing rates, high-frequency heart rate variability, and the number of position changes, were automatically computed using the Hexoskin sleep algorithm. Each night's features were then normalized using a reference night for each patient. A support vector machine classifier was trained for pseudo-prospective seizure-day forecasting, with forecasting horizons of 16- and 24-h to include both diurnal and nocturnal seizures (24-h) or diurnal seizures only (16-h). The algorithm's performance was assessed using a nested leave-one-patient-out cross-validation approach. RESULTS: Improvement over chance (IoC) performances were achieved for 48.7% and 40% of patients with the 16- and 24-h forecasting horizons, respectively. For patients with IoC performances, the proposed algorithm reached mean IoC, sensitivity and time in warning of 34.3%, 86.0%, and 51.7%, respectively for the 16-h horizon, and 34.2%, 64.4% and 30.2%, respectively, for the 24-h horizon. SIGNIFICANCE: Smart shirt-based nocturnal sleep analysis holds promise as a non-invasive approach for seizure-day forecasting in a subset of people with epilepsy. Further investigations, particularly in a residential setting with long-term recordings, could pave the way for the development of innovative and practical seizure forecasting devices. PLAIN LANGUAGE SUMMARY: Seizure forecasting with wearable devices may improve the quality of life of people living with epilepsy who experience unpredictable, recurrent seizures. In this study, we have developed a seizure forecasting algorithm using sleep characteristics obtained from a smart shirt worn at night by a large number of hospitalized patients with epilepsy (78). A daily seizure forecast was generated following each night using machine learning methods. Our results show that around half of people with epilepsy may benefit from such an approach.

Flexible Thermoelectric Wearable Architecture for Wireless Continuous Physiological Monitoring.

Continuous monitoring of physiological signals from the human body is critical in health monitoring, disease diagnosis, and therapeutics. Despite the needs, the existing wearable medical devices rely on either bulky wired systems or battery-powered devices needing frequent recharging. Here, we introduce a wearable, self-powered, thermoelectric flexible system architecture for wireless portable monitoring of physiological signals without recharging batteries. This system harvests an exceptionally high open circuit voltage of 175-180 mV from the human body, powering the wireless wearable bioelectronics to detect electrophysiological signals on the skin continuously. The thermoelectric system shows long-term stability in performance for 7 days with stable power management. Integrating screen printing, laser micromachining, and soft packaging technologies enables a multilayered, soft, wearable device to be mounted on any body part. The demonstration of the self-sustainable wearable system for detecting electromyograms and electrocardiograms captures the potential of the platform technology to offer various opportunities for continuous monitoring of biosignals, remote health monitoring, and automated disease diagnosis.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Clinician perceptions of a novel wearable robotic hand orthosis for post-stroke hemiparesis.

PURPOSE: Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care. METHODS: In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians' perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video. RESULTS: 154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time. CONCLUSIONS: Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders' perspectives on utility and design.

Clinicians valued both assistive and rehabilitative uses of a wearable robotic hand orthosis designed for individuals with hemiparesis after stroke.Wearable robotic hand devices should have the capacity to engage in functional, real-world activities for both assistive and rehabilitative purposes.Pragmatic factors, such as set-up and training time, must be balanced with device complexity to enable implementation in clinical settings.Stakeholders, such as clinicians, play an important role in identifying design priorities for wearable robotic devices to ensure these devices can meet the needs of end-users.

A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring.

Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), ß-cyclodextrin (ß-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with ß-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.

Wearable and Multiplexed Biosensors based on Oxide Field-Effect Transistors.

Wearable sensors designed for continuous, non-invasive monitoring of physicochemical signals are important for portable healthcare. Oxide field-effect transistor (FET)-type biosensors provide high sensitivity and scalability. However, they face challenges in mechanical flexibility, multiplexed sensing of different modules, and the absence of integrated on-site signal processing and wireless transmission functionalities for wearable sensing. In this work, a fully integrated wearable oxide FET-based biosensor array is developed to facilitate the multiplexed and simultaneous measurement of ion concentrations (H+, Na+, K+) and temperature. The FET-sensor array is achieved by utilizing a solution-processed ultrathin (≈6 nm thick) In2O3 active channel layer, exhibiting high compatibility with standard semiconductor technology, good mechanical flexibility, high uniformity, and low operational voltage of 0.005 V. This work provides an effective method to enable oxide FET-based biosensors for the fusion of multiplexed physicochemical information and wearable health monitoring applications.

All-in-One, Wireless, Multi-Sensor Integrated Athlete Health Monitor for Real-Time Continuous Detection of Dehydration and Physiological Stress.

Athletes are at high risk of dehydration, fatigue, and cardiac disorders due to extreme performance in often harsh environments. Despite advancements in sports training protocols, there is an urgent need for a non-invasive system capable of comprehensive health monitoring. Although a few existing wearables measure athlete's performance, they are limited by a single function, rigidity, bulkiness, and required straps and adhesives. Here, an all-in-one, multi-sensor integrated wearable system utilizing a set of nanomembrane soft sensors and electronics, enabling wireless, real-time, continuous monitoring of saliva osmolality, skin temperature, and heart functions is introduced. This system, using a soft patch and a sensor-integrated mouthguard, provides comprehensive monitoring of an athlete's hydration and physiological stress levels. A validation study in detecting real-time physiological levels shows the device's performance in capturing moments (400-500 s) of synchronized acute elevation in dehydration (350%) and physiological strain (175%) during field training sessions. Demonstration with a few human subjects highlights the system's capability to detect early signs of health abnormality, thus improving the healthcare of sports athletes.

Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.

PURPOSE: This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity. METHODS: For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models. RESULTS: The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253). CONCLUSIONS: The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.

Effects of wearable low-intensity continuous ultrasound on muscle biomechanical properties during delayed onset muscle soreness.

BACKGROUND: Delayed onset muscle soreness (DOMS) is one of the most prevalent musculoskeletal symptoms in individuals engaged in strenuous exercise programs. OBJECTIVE: This study investigated the effects of wearable low-intensity continuous ultrasound on muscle biomechanical properties during DOMS. METHODS: Twenty volunteers were distributed into a wearable ultrasound stimulation group (WUG) (n= 10) and medical ultrasound stimulation group (MUG) (n= 10). All subjects performed wrist extensor muscle strength exercises to induce DOMS. At the site of pain, ultrasound of frequency 3 MHz was applied for 1 h or 5 min in each subject of the WUG or MUG, respectively. Before and after ultrasound stimulation, muscle biomechanical properties (tone, stiffness, elasticity, stress relaxation time, and creep) and body temperature were measured, and pain was evaluated. RESULTS: A significant decrease was found in the tone, stiffness, stress relaxation time, and creep in both groups after ultrasound stimulation (all p< 0.05). A significant decrease in the pain and increases in temperature were observed in both groups (all p< 0.05). No significant differences were observed between the groups in most evaluations. CONCLUSION: The stiffness and pain caused by DOMS were alleviated using a wearable ultrasound stimulator. Furthermore, the effects of the wearable ultrasound stimulator were like those of a medical ultrasound stimulator.

A wearable non-enzymatic sensor for continuous monitoring of glucose in human sweat.

To enhance personalized diabetes management, there is a critical need for non-invasive wearable electrochemical sensors made from flexible materials to enable continuous monitoring of sweat glucose levels. The main challenge lies in developing glucose sensors with superior electrochemical characteristics and high adaptability. Herein, we present a wearable sensor for non-enzymatic electrochemical glucose analysis. The sensor was synthesized using hydrothermal and one-pot preparation methods, incorporating gold nanoparticles (AuNPs) functionalized onto aminated multi-walled carbon nanotubes (AMWCNTs) as an efficient catalyst, and crosslinked with carboxylated styrene butadiene rubber (XSBR) and PEDOT:PSS. The sensors were then integrated onto screen-printed electrodes (SPEs) to create flexible glucose sensors (XSBR-PEDOT:PSS-AMWCNTs/AuNPs/SPE). Operating under neutral conditions, the sensor exhibits a linear range of 50 µmol/L to 600 µmol/L, with a limit of detection limit of 3.2 µmol/L (S/N = 3), enabling the detection of minute glucose concentrations. The flexible glucose sensor maintains functionality after 500 repetitions of bending at a 180° angle, without significant degradation in performance. Furthermore, the sensor exhibits exceptional stability, repeatability, and resistance to interference. Importantly, we successfully monitored changes in sweat glucose levels by applying screen-printed electrodes to human skin, with results consistent with normal physiological blood glucose fluctuations. This study details the fabrication of a wearable sensor characterized by ease of manufacture, remarkable flexibility, high sensitivity, and adaptability for non-invasive blood glucose monitoring through non-enzymatic electrochemical analysis. Thus, this streamlined fabrication process presents a novel approach for non-invasive, real-time blood glucose level monitoring.

Self-assembly of cascade nanoenzyme glucose oxidase encapsulated in copper benzenedicarboxylate for wearable sweat-glucose colorimetric sensors with smartphone readout.

BACKGROUND: With the advent of personalized medical approaches, precise and tailored treatments are expected to become widely accepted for the prevention and treatment of diabetes. Paper-based colorimetric sensors that function in combination with smartphones have been rapidly developed in recent years because it does not require additional equipment and is inexpensive and easy to perform. In this study, we developed a portable, low-cost, and wearable sweat-glucose detection device for in situ detection. RESULTS: The sensor adopted an integrated biomimetic nanoenzyme of glucose oxidase (GOx) encapsulated in copper 1, 4-benzenedicarboxylate (CuBDC) (GOx@CuBDC) through a biomimetic mineralization process. CuBDC exhibited a peroxide-like effect, cascade catalytic effect with the encapsulated GOx, and increased the enzyme stability. GOx@CuBDC and 3,3,5,5-tetramethylbenzidine were combined to form a hybrid membrane that achieved single-step paper-based glucose detection. SIGNIFICANCE AND NOVELTY: This GOx@CuBDC-based colorimetric glucose sensor was used to quantitatively analyze the sweat-glucose concentration with smartphone readings. The sensor exhibited a good linear relationship over the concentration range of 40-900 µM and a limit of detection of 20.7 µM (S/N = 3). Moreover, the sensor performed well in situ monitoring and in evaluating variations based on the consumption of foods with different glycemic indices. Therefore, the fabricated wearable sweat-glucose sensors exhibited optimal practical application performance.

Deep learning-driven dyslexia detection model using multi-modality data.

Background: Dyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. Methods: In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results: The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

Digital health intervention in patients undergoing cardiac rehabilitation: systematic review and meta-analysis.

Background: Cardiovascular disease (CVD) continues to be the foremost mortality internationally. Cardiac rehabilitation has proven as an effective program in reducing CVD burden. Participation in cardiac rehabilitation programs is very low. Digital health intervention emerged as an alternative method to deliver Cardiac rehabilitation. This review aimed to investigate the impact of digital health intervention on the outcomes of interest. Methods: the following databases: PubMed, CINAHL, Scopus, and Cochrane Library have been searched to retrieve randomized controlled trials that examine the impact of digital health intervention on blood pressure, body mass index, lipid profile, blood glucose, Six-Minute Walk Test, and peak oxygen consumption. filters were set to include studies published in English between 2000-2023. Results: Nineteen studies were included in this review. Six-Minute Walk Test (MD = 16.70; 95% CI: 6.00 to 27.39, p = 0.000) and maximal oxygen consumption (SMD = 0.27; 95% CI: 0.08 to 0.45, p = 0.004) significantly improved following digital health intervention, after employing the sensitivity analysis significant improvement was observed in systolic (MD = -2.54; 95% CI: -4.98 to -0.11, p = 0.04) and diastolic blood pressure (SMD = -2.0182; 95% CI: -3.9436 to -0.0928, p = 0.04) favoring experimental groups. Subgroup analysis revealed significant improvement in quality of life after three months of follow-up (SMD = 0.18; 95% CI: 0.05 to 0.31, p = 0.00), no significant differences have been observed in body mass index, lipid profile, and blood glucose. Conclusion: The findings emphasize the significant impact of digital vs CBCR or usual care on physical capacity, blood pressure, and quality of life. Despite the non-statistically significant differences in body mass index and lipid profile, the comparable effect between the two methods suggests the superiority of digital over CBCR or usual care due to its convenient nature, accessibility, and cost-effectiveness.

Why do generation X customers use wearable fitness technology equipment after recovering from coronavirus? The role of perceived health risks.

The health sector has prioritized the physical health of vulnerable Generation X individuals at high Coronavirus risk. Despite vaccination efforts, both infected and healthy people continue facing health threats. Unlike other industries devastated by COVID-19, wearable fitness technology equipment (WFTE) is essential for health-focused individuals. This research examined customers' intention to use WFTE using an adapted Technology Acceptance Model (TAM) framework. A key contribution is the inclusion of perceived health risk and its impact on WFTE value perceptions and usage attitudes post-pandemic. The study gathered qualitative data from coronavirus patients and survey data from 513 participants. Structural equation modeling analysis supported the theoretical model. While the standard TAM evaluated intent to use WFTE, this study uniquely examined how WFTE's functional, hedonic, and symbolic value shapes its perceived value. Perceived health risk was found to significantly impact perceived WFTE value and usage attitudes after the pandemic recovery. Findings offer managerial implications to boost WFTE adoption among the vulnerable Generation X demographic.

Multiscale Bowel Sound Event Spotting in Highly Imbalanced Wearable Monitoring Data: Algorithm Development and Validation Study.

BACKGROUND: Abdominal auscultation (i.e., listening to bowel sounds (BSs)) can be used to analyze digestion. An automated retrieval of BS would be beneficial to assess gastrointestinal disorders noninvasively. OBJECTIVE: This study aims to develop a multiscale spotting model to detect BSs in continuous audio data from a wearable monitoring system. METHODS: We designed a spotting model based on the Efficient-U-Net (EffUNet) architecture to analyze 10-second audio segments at a time and spot BSs with a temporal resolution of 25 ms. Evaluation data were collected across different digestive phases from 18 healthy participants and 9 patients with inflammatory bowel disease (IBD). Audio data were recorded in a daytime setting with a smart T-Shirt that embeds digital microphones. The data set was annotated by independent raters with substantial agreement (Cohen κ between 0.70 and 0.75), resulting in 136 hours of labeled data. In total, 11,482 BSs were analyzed, with a BS duration ranging between 18 ms and 6.3 seconds. The share of BSs in the data set (BS ratio) was 0.0089. We analyzed the performance depending on noise level, BS duration, and BS event rate. We also report spotting timing errors. RESULTS: Leave-one-participant-out cross-validation of BS event spotting yielded a median F1-score of 0.73 for both healthy volunteers and patients with IBD. EffUNet detected BSs under different noise conditions with 0.73 recall and 0.72 precision. In particular, for a signal-to-noise ratio over 4 dB, more than 83% of BSs were recognized, with precision of 0.77 or more. EffUNet recall dropped below 0.60 for BS duration of 1.5 seconds or less. At a BS ratio greater than 0.05, the precision of our model was over 0.83. For both healthy participants and patients with IBD, insertion and deletion timing errors were the largest, with a total of 15.54 minutes of insertion errors and 13.08 minutes of deletion errors over the total audio data set. On our data set, EffUNet outperformed existing BS spotting models that provide similar temporal resolution. CONCLUSIONS: The EffUNet spotter is robust against background noise and can retrieve BSs with varying duration. EffUNet outperforms previous BS detection approaches in unmodified audio data, containing highly sparse BS events.

A standardised approach to quantifying activity in domestic dogs.

Objective assessment of activity via accelerometry can provide valuable insights into dog health and welfare. Common activity metrics involve using acceleration cut-points to group data into intensity categories and reporting the time spent in each category. Lack of consistency and transparency in cut-point derivation makes it difficult to compare findings between studies. We present an alternative metric for use in dogs: the acceleration threshold (as a fraction of standard gravity, 1 g = 9.81 m/s2) above which the animal's X most active minutes are accumulated (MXACC) over a 24-hour period. We report M2ACC, M30ACC and M60ACC data from a colony of healthy beagles (n = 6) aged 3-13 months. To ensure that reference values are applicable across a wider dog population, we incorporated labelled data from beagles and volunteer pet dogs (n = 16) of a variety of ages and breeds. The dogs' normal activity patterns were recorded at 200 Hz for 24 hours using collar-based Axivity-AX3 accelerometers. We calculated acceleration vector magnitude and MXACC metrics. Using labelled data from both beagles and pet dogs, we characterize the range of acceleration outputs exhibited enabling meaningful interpretation of MXACC. These metrics will help standardize measurement of canine activity and serve as outcome measures for veterinary and translational research.

Cardiac arrhythmia detection using artificial neural network.

This paper outlines the development of the 'Cardiac Abnormality Monitoring' wearable medical device, aimed at creating a compact safety monitor integrating advanced Artificial Neural Network (ANN) algorithms. Given power consumption constraints and cost-effectiveness, a strategy combining sophisticated instruments with neural network algorithms is proposed to enhance performance. This approach aims to compete with high-end wearable devices, utilizing innovative manufacturing techniques. The paper evaluates the feasibility of employing the Levenberg-Marquardt (LM) ANN algorithm in power-conscious wearable devices, considering its potential for offline embedded systems or IoT gadgets capable of cloud-based data uploading. The Levenberg-Marquardt ANN is chosen primarily for its practicality in prototype development, with other neural network algorithms also explored to identify potential alternatives. We have compared the six neural network models and determined the model that has the potential to replace the primary neural network model. We found that the 'Kernelized SVC with PCA' can test accuracy. To be specific, in this paper, we will evaluate the performance of the ANN model and also check its feasibility and practicality by integrating it with a constructed prototypical working model.

Accuracy and completeness of autobiographical memory: evidence from a wearable camera study.

A small wearable camera, SenseCam, passively captured pictures from everyday experience that were later used to evaluate the accuracy and completeness of autobiographical memory. Nine undergraduates wore SenseCams that took pictures every 10 s for two days. After one week and one month, participants first recalled their experiences from specific time periods (timeslices), then reviewed the corresponding pictures to make corrections and report information omitted from initial recall. Results demonstrated the utility of wearable cameras as research tools, and illustrated several characteristics of everyday memory. Recall contents reflected the structure of undergraduate lives. Three different types of omissions were reported: neglected, reminded, and forgotten. Pictures stimulated memory, even for non-visual information (e.g., feelings, thoughts), increasing recall by 23%. The mean completeness of initial recall was 79% (upper bound), with at least 21% forgetting. Accuracy was self-scored by participants (M = 89%), and the mean error rate (11%) provided evidence against strong reconstructive and copy theories of memory. The characteristics of errors shed light on the cognitive processes underlying them. Ratings of recall (confidence, reliving, knowledge, and frequency) supported the episodic/semantic distinction, the dual-process theory of repetition, and reconstructive imagery. Metamemory measures showed a positive correlation between confidence and accuracy.

Stretchable Iontronic Tactile Sensing Fabric.

The iontronic tactile sensing modality has garnered significant attention due to its exceptional sensitivity, immunity to noise, and versatility in materials. Recently, various formats of iontronic tactile sensors have been developed, including droplets, polymer films, paper, ionic gels, and fabrics. However, the stretchability of the current iontronic pressure sensing fabric is inadequate, hindered by the limited stretchiness of the ionic functional fabric. Incorporating a stretchable tactile sensing implement could enhance the wear comfortability by preventing relative movement and ensuring intimate contact between the sensor and the skin. The research focuses on the development of a stretchable iontronic pressure sensing (SIPS) fabric for monitoring diverse aspects of body health and movement in wearable applications. The tactile sensing structure is generated at the iontronic interface between highly stretchable ionic and conductive fabrics. In particular, the ionic fabric is prepared by coating a layer of polyurethane/ionic liquid gel onto a Spandex fabric. To showcase its remarkable sensitivity, stretchability, and ability to detect diverse body information, several application scenarios have been demonstrated including an elastic wristband for precise pulse wave detection, a flexible belt with multitactile sensing channels for respiration and motion tracking purposes, and a stretchable fabric cuff equipped with a high-resolution sensing array comprising 32 × 32 units for accurate gesture recognition.