Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

Cloud Native Remote Monitoring Data Ecosystem for Aging Population based on Commercial AAL Sensors.

In the recent years, Active Assisted Living (AAL) technologies used for autonomous tracking and activity recognition have started to play major roles in geriatric care. From fall detection to remotely monitoring behavioral patterns, vital functions and collection of air quality data, AAL has become pervasive in the modern era of independent living for the elderly section of the population. However, even with the current rate of progress, data access and data reliability has become a major hurdle especially when such data is intended to be used in new age modelling approaches such as those using machine learning. This paper presents a comprehensive data ecosystem comprising remote monitoring AAL sensors along with extensive focus on cloud native system architecture, secured and confidential access to data with easy data sharing. Results from a validation study illustrate the feasibility of using this system for remote healthcare surveillance. The proposed system shows great promise in multiple fields from various AAL studies to development of data driven policies by local governments in promoting healthy lifestyles for the elderly alongside a common data repository that can be beneficial to other research communities worldwide.Clinical Relevance- This study creates a cloud-based smart home data ecosystem, which can achieve the remote healthcare monitoring for aging population, enabling them to live more independently and decreasing hospital admission rates.

PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers.

After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb "πι´πτω", signifying "to fall"), is open sourced in Python and C.

Validity of activity wristbands for estimating daily physical activity in primary schoolchildren under free-living conditions: School-Fit study.

Introduction: The use of activity wristbands to monitor and promote schoolchildren's physical activity (PA) is increasingly widespread. However, their validity has not been sufficiently studied, especially among primary schoolchildren. Consequently, the main purpose was to examine the validity of the daily steps and moderate-to-vigorous PA (MVPA) scores estimated by the activity wristbands Fitbit Ace 2, Garmin Vivofit Jr 2, and the Xiaomi Mi Band 5 in primary schoolchildren under free-living conditions. Materials and methods: An initial sample of 67 schoolchildren (final sample = 62; 50% females), aged 9-12 years old (mean = 10.4 ± 1.0 years), participated in the present study. Each participant wore three activity wristbands (Fitbit Ace 2, Garmin Vivofit Jr 2, and Xiaomi Mi Band 5) on his/her non-dominant wrist and a research-grade accelerometer (ActiGraph wGT3X-BT) on his/her hip as the reference standard (number of steps and time in MVPA) during the waking time of one day. Results: Results showed that the validity of the daily step scores estimated by the Garmin Vivofit Jr 2 and Xiaomi Mi Band 5 were good and acceptable (e.g., MAPE = 9.6/11.3%, and lower 95% IC of ICC = 0.87/0.73), respectively, as well as correctly classified schoolchildren as meeting or not meeting the daily 10,000/12,000-step-based recommendations, obtaining excellent/good and good/acceptable results (e.g., Garmin Vivofit Jr 2, k = 0.75/0.62; Xiaomi Mi Band 5, k = 0.73/0.53), respectively. However, the Fitbit Ace 2 did not show an acceptable validity (e.g., daily steps: MAPE = 21.1%, and lower 95% IC of ICC = 0.00; step-based recommendations: k = 0.48/0.36). None of the three activity wristbands showed an adequate validity for estimating daily MVPA (e.g., MAPE = 36.6-90.3%, and lower 95% IC of ICC = 0.00-0.41) and the validity for the MVPA-based recommendation tended to be considerably lower (e.g., k = -0.03-0.54). Conclusions: The activity wristband Garmin Vivofit Jr 2 obtained the best validity for monitoring primary schoolchildren's daily steps, offering a feasible alternative to the research-grade accelerometers. Furthermore, this activity wristband could be used during PA promotion programs to provide accurate feedback to primary schoolchildren to ensure their accomplishment with the PA recommendations.

Patient and clinician perspectives of a remote monitoring program for COVID-19 and lessons for future programs.

COVID Watch is a remote patient monitoring program implemented during the pandemic to support home dwelling patients with COVID-19. The program conferred a large survival advantage. We conducted semi-structured interviews of 85 patients and clinicians using COVID Watch to understand how to design such programs even better. Patients and clinicians found COVID Watch to be comforting and beneficial, but both groups desired more clarity about the purpose and timing of enrollment and alternatives to text-messages to adapt to patients' preferences as these may have limited engagement and enrollment among marginalized patient populations. Because inclusiveness and equity are important elements of programmatic success, future programs will need flexible and multi-channel human-to-human communication pathways for complex clinical interactions or for patients who do not desire tech-first approaches.

Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning.

Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.

Home nocturnal infrared video to record non-rapid eye movement sleep parasomnias.

To assess the feasibility, the acceptability and the usefulness of home nocturnal infrared video in recording the frequency and the complexity of non-rapid eye movement sleep parasomnias in adults, and in monitoring the treatment response. Twenty adult patients (10 males, median age 27.5 years) with a diagnosis of non-rapid eye movement parasomnia were consecutively enrolled. They had a face-to-face interview, completed self-reported questionnaires to assess clinical characteristics and performed a video-polysomnography in the Sleep Unit. Patients were then monitored at home during at least five consecutive nights using infrared-triggered cameras. They completed a sleep diary and questionnaires to evaluate the number of parasomniac episodes at home and the acceptability of the home nocturnal infrared video recording. Behavioural analyses were performed on home nocturnal infrared video and video-polysomnography recordings. Eight patients treated by clonazepam underwent a second home nocturnal infrared video recording during five consecutive days. All patients had at least one parasomniac episode during the home nocturnal infrared video monitoring, compared with 75% during the video-polysomnography. A minimum of three consecutive nights with home nocturnal infrared video was required to record at least one parasomniac episode. Most patients underestimated the frequency of episodes on the sleep diary compared with home nocturnal infrared video. Episodes recorded at home were often more complex than those recorded during the video-polysomnography. The user-perceived acceptability of the home nocturnal infrared video assessment was excellent. The frequency and the complexity of the parasomniac episodes decreased with clonazepam. Home nocturnal infrared video has good feasibility and acceptability, and may improve the evaluation of the phenotype and severity of the non-rapid eye movement parasomnias and of the treatment response in an ecological setting.

Detection of Parkinson's Disease Using Wrist Accelerometer Data and Passive Monitoring.

Parkinson's disease is a neurodegenerative disorder impacting patients' movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population's movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson's disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.

Continuous Long-Term Physical Activity Monitoring in Hemodialysis Patients.

Background: Physical inactivity is common in patients receiving hemodialysis, but activity patterns throughout the day and in relation to dialysis are largely unknown. This knowledge gap can be addressed by long-term continuous activity monitoring, but this has not been attempted and may not be acceptable to patients receiving dialysis. Methods: Ambulatory patients with end-stage kidney disease receiving thrice-weekly hemodialysis wore commercially available wrist-worn activity monitors for 6 months. Step counts were collected every 15 minutes and were linked to dialysis treatments. Physical function was assessed using the Short Physical Performance Battery (SPPB). Fast time to recovery from dialysis was defined as ≤2 hours. Mixed effects models were created to estimate step counts over time. Results: Of 52 patients enrolled, 48 were included in the final cohort. The mean age was 60 years, and 75% were Black or Hispanic. Comorbidity burden was high, 38% were transported to and from dialysis by paratransit, and 79% had SPPB <10. Median accelerometer use (199 days) and adherence (95%) were high. Forty-two patients (of 43 responders) reported wearing the accelerometer every day, and few barriers to adherence were noted. Step counts were lower on dialysis days (3991 [95% CI, 3187 to 4796] versus 4561 [95% CI, 3757 to 5365]), but step-count intensity was significantly higher during the hour immediately after dialysis than during the corresponding time on nondialysis days (188 steps per hour increase [95% CI, 171 to 205]); these levels were the highest noted at any time. Postdialysis increases were more pronounced among patients with fast recovery time (225 [95% CI, 203 to 248] versus 134 [95% CI, 107 to 161] steps per hour) or those with SPPB ≥7. Estimates were unchanged after adjustment for demographics, diabetes status, and ultrafiltration rate. Conclusions: Long-term continuous monitoring of physical activity is feasible in patients receiving hemodialysis. Highly granular data collection and analysis yielded new insights into patterns of activity after dialysis treatments.

A Human Gait Tracking System Using Dual Foot-Mounted IMU and Multiple 2D LiDARs.

This paper proposes a human gait tracking system using a dual foot-mounted IMU and multiple 2D LiDARs. The combining system aims to overcome the disadvantages of each single sensor system (the short tracking range of the single 2D LiDAR and the drift errors of the IMU system). The LiDARs act as anchors to mitigate the errors of an inertial navigation algorithm. In our system, two 2D LiDARs are used. LiDAR 1 is placed around the starting point, and LiDAR 2 is placed at the ending point (in straight walking) or at the turning point (in rectangular path walking). Using the LiDAR 1, we can estimate the initial headings and positions of each IMU without any calibration process. We also propose a method to calibrate two LiDARs that are placed far apart. Then, the measurement from two LiDARs can be combined in a Kalman filter and the smoother algorithm to correct the two estimated feet trajectories. If straight walking is detected, we update the current stride heading and the foot position using the previous stride headings. Then, it is used as a measurement update in the Kalman filter. In the smoother algorithm, a step width constraint is used as a measurement update. We evaluate the stride length estimation through a straight walking experiment along a corridor. The root mean square errors compared with an optical tracking system are less than 3 cm. The performance of proposed method is also verified with a rectangular path walking experiment.

[Technical Difficulties in the Implementation of the Continuous Glucose Monitoring System].

The continuous glucose monitoring system (CGMS) has been clinically applied to monitor the dynamic change of the subcutaneous interstitial glucose concentration which is a function of the blood glucose level by glucose sensors. It can track blood glucose levels all day along, and thus provide comprehensive and reliable information about blood glucose dynamics. The clinical application of CGMS enables monitoring of blood glucose fluctuations and the discovery of hidden hyperglycemia and hypoglycemia that are difficult to be detected by traditional methods. As a CGMS needs to work subcutaneously for a long time, a series of factors such as biocompatibility, enzyme inactivation, oxygen deficiency, foreign body reaction, implant size, electrode flexibility, error correction, comfort, device toxicity, electrical safety, et al. should be considered beforehand. The study focused on the difficulties in the technology, and compared the products of Abbott, Medtronic and DexCom, then summarized their cutting-edge. Finally, this study expounded some key technologies in dynamic blood glucose monitoring and therefore can be utilized as a reference for the development of CGMS.

Application of Accelerometer to Monitor Students' Exercise Load in 50 m Round Trip.

With the further advancement of microelectronics innovation and sensors, sensors can be broadly implanted in cell phone gadgets, compact gadgets, and so forth. The utilization of speed increase sensors for human running checking has expansive application possibilities. From one perspective, the everyday development of the human body is firmly connected with the physical and emotional wellness of the person. Observing the day-to-day developments of the human body is of incredible importance in planning a logical running activity plan and working on actual wellbeing. On the other hand, it is also of practical value to monitor human abnormal movements. This kind of abnormal movement caused by accidental falls can bring certain harm to the human body. Real-time monitoring of the fall can provide timely assistance to the person and reduce the risk brought by the fall. This article analyzes and summarizes the research theories and common research methods in the field of 50 m round-trip movement monitoring based on the acceleration sensor. According to the process of 50 m round-trip movement pattern recognition, the data collection, preprocessing, feature extraction, and selection of 50 m round-trip movement are evaluated. The classification and recognition of each module were analyzed. This article proposes a human body motion recognition mechanism based on acceleration sensors by looking at the three trademark upsides, the wavefront edge, wavefront limit, and time stretch between the pinnacle and valley of the speed increase sensor vertical information waveform, and joining the rule of choice tree order to accomplish the activities of hunching down, taking off, and running. To get an accurate recognizable proof and recognize ways of behaving, a human fall identification calculation is proposed. This calculation removes human movement attributes throughout the fall and focuses on four sorts of falls: forward fall, reverse fall, left fall, and right fall by utilizing the connection of the three tomahawks of the speed increase sensor. The trial results show that the normal right acknowledgment pace of the human body's 50 m full-circle running way of behaviour is more than 90%, which has specific useful application esteem.

Effect of Electronic Symptom Monitoring on Patient-Reported Outcomes Among Patients With Metastatic Cancer: A Randomized Clinical Trial.

Importance: Electronic systems that facilitate patient-reported outcome (PRO) surveys for patients with cancer may detect symptoms early and prompt clinicians to intervene. Objective: To evaluate whether electronic symptom monitoring during cancer treatment confers benefits on quality-of-life outcomes. Design, Setting, and Participants: Report of secondary outcomes from the PRO-TECT (Alliance AFT-39) cluster randomized trial in 52 US community oncology practices randomized to electronic symptom monitoring with PRO surveys or usual care. Between October 2017 and March 2020, 1191 adults being treated for metastatic cancer were enrolled, with last follow-up on May 17, 2021. Interventions: In the PRO group, participants (n = 593) were asked to complete weekly surveys via an internet-based or automated telephone system for up to 1 year. Severe or worsening symptoms triggered care team alerts. The control group (n = 598) received usual care. Main Outcomes and Measures: The 3 prespecified secondary outcomes were physical function, symptom control, and health-related quality of life (HRQOL) at 3 months, measured by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (QLQ-C30; range, 0-100 points; minimum clinically important difference [MCID], 2-7 for physical function; no MCID defined for symptom control or HRQOL). Results on the primary outcome, overall survival, are not yet available. Results: Among 52 practices, 1191 patients were included (mean age, 62.2 years; 694 [58.3%] women); 1066 (89.5%) completed 3-month follow-up. Compared with usual care, mean changes on the QLQ-C30 from baseline to 3 months were significantly improved in the PRO group for physical function (PRO, from 74.27 to 75.81 points; control, from 73.54 to 72.61 points; mean difference, 2.47 [95% CI, 0.41-4.53]; P = .02), symptom control (PRO, from 77.67 to 80.03 points; control, from 76.75 to 76.55 points; mean difference, 2.56 [95% CI, 0.95-4.17]; P = .002), and HRQOL (PRO, from 78.11 to 80.03 points; control, from 77.00 to 76.50 points; mean difference, 2.43 [95% CI, 0.90-3.96]; P = .002). Patients in the PRO group had significantly greater odds of experiencing clinically meaningful benefits vs usual care for physical function (7.7% more with improvements of ≥5 points and 6.1% fewer with worsening of ≥5 points; odds ratio [OR], 1.35 [95% CI, 1.08-1.70]; P = .009), symptom control (8.6% and 7.5%, respectively; OR, 1.50 [95% CI, 1.15-1.95]; P = .003), and HRQOL (8.5% and 4.9%, respectively; OR, 1.41 [95% CI, 1.10-1.81]; P = .006). Conclusions and Relevance: In this report of secondary outcomes from a randomized clinical trial of adults receiving cancer treatment, use of weekly electronic PRO surveys to monitor symptoms, compared with usual care, resulted in statistically significant improvements in physical function, symptom control, and HRQOL at 3 months, with mean improvements of approximately 2.5 points on a 0- to 100-point scale. These findings should be interpreted provisionally pending results of the primary outcome of overall survival. Trial Registration: ClinicalTrials.gov Identifier: NCT03249090.

Validity of the ActivPAL monitor to distinguish postures: A systematic review.

BACKGROUND: Posture has been recently integrated into activity guidelines, advising people to limit their sedentary time and break up sedentary postures with standing/stepping as much as possible. The thigh-worn activPAL is a frequently used objective measure of posture, but its validity has only been investigated by individual studies and has not been systematically reviewed. RESEARCH QUESTION: Can the activPAL accurately characterize different postures? METHODS: A rigorous systematic review protocol was conducted, including multiple study screeners and determiners of study quality. To be included, validation studies had to examine the accuracy of an activPAL posture outcome relative to a criterion measure (e.g., direct observation) in adults (>18 years). Citations were not restricted to language or date of publication. Sources were searched on May 16, 2021 and included Scopus, EMBASE, MEDLINE, CINAHL, and Academic Search Premier. The study was pre-registered in Prospero (ID# CRD42021248240). Study quality was determined using a modified Hagströmer Bowles checklist. The results are presented narratively. RESULTS: Twenty-four studies (18 semi-structured laboratory arms, 8 uncontrolled protocol arms; 476 participants) met the inclusion criteria. Some studies (5/24) incorporated dual-monitor (trunk: 4/5; shin: 1/5) configurations. While heterogenous statistical procedures were implemented, most studies (n = 22/24) demonstrated a high validity (e.g., percent agreement >90%, no fixed bias, etc.) of the activPAL to measure sedentary and/or upright postures across semi-structured (17/18 arms) and uncontrolled study designs (7/8 arms). Specific experimental protocol factors (i.e., seat height, fidgeting, non-direct observation criterion comparator) likely explain the divergent reports that observed valid versus invalid findings. The study quality was 11.3 (standard deviation: 2.3) out of 19. CONCLUSION: Despite heterogeneous methodological and statistical approaches, the included studies generally provide supporting evidence that the activPAL can accurately distinguish between sedentary and standing postures. Multiple activPAL monitor configurations (e.g., thigh and torso) are needed to better characterize sitting versus lying postures.

Transdermal sensor features correlate with ecological momentary assessment drinking reports and predict alcohol-related consequences in young adults' natural settings.

BACKGROUND: Wearable transdermal alcohol concentration (TAC) sensors allow passive monitoring of alcohol concentration in natural settings and measurement of multiple features from drinking episodes, including peak intoxication level, speed of intoxication (absorption rate) and elimination, and duration. These passively collected features extend commonly used self-reported drink counts and may facilitate the prediction of alcohol-related consequences in natural settings, aiding risk stratification and prevention efforts. METHOD: A total of 222 young adults aged 21-29 (M age = 22.3, 64% female, 79% non-Hispanic white, 84% undergraduates) who regularly drink heavily participated in a 5-day study that included the ecological momentary assessment (EMA) of alcohol consumption (daily morning reports and participant-initiated episodic EMA sequences) and the wearing of TAC sensors (SCRAM-CAM anklets). The analytic sample contained 218 participants and 1274 days (including 554 self-reported drinking days). Five features-area under the curve (AUC), peak TAC, rise rate (rate of absorption), fall rate (rate of elimination), and duration-were extracted from TAC-positive trajectories for each drinking day. Day- and person-level associations of TAC features with drink counts (morning and episodic EMA) and alcohol-related consequences were tested using multilevel modeling. RESULTS: TAC features were strongly associated with morning drink reports (r = 0.6-0.7) but only moderately associated with episodic EMA drink counts (r = 0.3-0.5) at both day and person levels. Higher peaks, larger AUCs, faster rise rates, and faster fall rates were significantly predictive of day-level alcohol-related consequences after adjusting for both morning and episodic EMA drink counts in separate models. Person means of TAC features added little above daily scores to the prediction of alcohol-related consequences. CONCLUSIONS: These results support the utility of TAC sensors in studies of alcohol misuse among young adults in natural settings and outline the specific TAC features that contribute to the day-level prediction of alcohol-related consequences. TAC sensors provide a passive option for obtaining valid and unique information predictive of drinking risk in natural settings.

Non-adjunctive continuous glucose monitoring for control of hypoglycaemia (COACH): Results of a post-approval observational study.

OBJECTIVE: Prior to the Continuous Monitoring and Control of Hypoglycaemia (COACH) study described herein, no study had been powered to evaluate the impact of non-adjunctive RT-CGM use on the rate of debilitating moderate or severe hypoglycaemic events. RESEARCH DESIGN AND METHODS: In this 12-month observational study, adults with insulin-requiring diabetes who were new to RT-CGM participated in a 6-month control phase where insulin dosing decisions were based on self monitoring of blood glucose values, followed by a 6-month phase where decisions were based on RT-CGM data (i.e. non-adjunctive RT-CGM use); recommendations for RT-CGM use were made according to sites' usual care. The primary outcome was change in debilitating moderate (requiring second-party assistance) and severe (resulting in seizures or loss of consciousness) hypoglycaemic event frequency. Secondary outcomes included changes in HbA1c and diabetic ketoacidosis (DKA) frequency. RESULTS: A total of 519 participants with mean (SD) age 50.3 (16.1) years and baseline HbA1c 8.0% (1.4%) completed the study, of whom 32.8% had impaired hypoglycaemia awareness and 33.5% had type 2 diabetes (T2D). The mean (SE) per-patient frequency of hypoglycaemic events decreased by 63% from 0.08 (0.016) during the SMBG phase to 0.03 (0.010) during the RT-CGM phase (p = 0.005). HbA1c decreased during the RT-CGM phase both for participants with type 1 diabetes (T1D) and T2D and there was a trend towards larger reductions among individuals with higher baseline HbA1c. CONCLUSIONS: Among adults with insulin-requiring diabetes, non-adjunctive use of RT-CGM data is safe, resulting in significantly fewer debilitating hypoglycaemic events than management using SMBG.

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost-effective human-machine interface to enhance the immersion during the long-term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG-based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower-limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real-time robotic manipulation and virtual game for immersion-enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower-limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming-aided training, showing its potential in IoT-based smart healthcare applications.