Tremor Suppression Using Functional Electrical Stimulation.

Parkinson's disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management methods such as medication and deep brain surgery, non invasive tremor suppression methods have become more popular in recent years. Functional electrical stimulation (FES) is one of the methods used to reduce tremor in several studies. However, the effect of different FES parameters on tremor suppression and discomfort level, including amplitude, the number of pulses in each stimulation burst, frequency, and pulse width is yet to be studied for longer stimulation durations. Therefore, in this work, experiments were performed on 14 participants with PD to evaluate the effect of thirty seconds of out-of-phase electrical stimulation on wrist tremor at rest. Trials were conducted by varying the stimulation amplitude and the number of pulses while keeping the frequency and pulse width constant. Each test was repeated three times for each participant. The results showed an overall tremor suppression for 11 out of 14 participants and no average positive effects for three participants. It is concluded that despite the effectiveness of FES in tremor suppression, each set of FES parameters showed different suppression levels among participants due to the variability of tremor over time. Thus, for this method to be effective, an adaptive control system would be required to tune FES parameters in real time according to changes in tremor during extended stimulation periods.

A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles.

Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.

Preliminary Evaluation of New Wearable Sensors to Study Incongruous Postures Held by Employees in Viticulture.

Musculoskeletal Disorders (MSDs) stand as a prominent cause of injuries in modern agriculture. Scientific research has highlighted a causal link between MSDs and awkward working postures. Several methods for the evaluation of working postures, and related risks, have been developed such as the Rapid Upper Limb Assessment (RULA). Nevertheless, these methods are generally applied with manual measurements on pictures or videos. As a consequence, their applicability could be scarce, and their effectiveness could be limited. The use of wearable sensors to collect kinetic data could facilitate the use of these methods for risk assessment. Nevertheless, the existing system may not be usable in the agricultural and vine sectors because of its cost, robustness and versatility to the various anthropometric characteristics of workers. The aim of this study was to develop a technology capable of collecting accurate data about uncomfortable postures and repetitive movements typical of vine workers. Specific objectives of the project were the development of a low-cost, robust, and wearable device, which could measure data about wrist angles and workers' hand positions during possible viticultural operations. Furthermore, the project was meant to test its use to evaluate incongruous postures and repetitive movements of workers' hand positions during pruning operations in vineyard. The developed sensor had 3-axis accelerometers and a gyroscope, and it could monitor the positions of the hand-wrist-forearm musculoskeletal system when moving. When such a sensor was applied to the study of a real case, such as the pruning of a vines, it permitted the evaluation of a simulated sequence of pruning and the quantification of the levels of risk induced by this type of agricultural activity.

Low-Cost Dynamometer for Measuring and Regulating Wrist Extension and Flexion Motor Tasks in Electroencephalography Experiments.

A brain-computer interface could control a bionic hand by interpreting electroencephalographic (EEG) signals associated with wrist extension (WE) and wrist flexion (WF) movements. Misinterpretations of the EEG may stem from variations in the force, speed and range of these movements. To address this, we designed, constructed and tested a novel dynamometer, the IsoReg, which regulates WE and WF movements during EEG recording experiments. The IsoReg restricts hand movements to isometric WE and WF, controlling their speed and range of motion. It measures movement force using a dual-load cell system that calculates the percentage of maximum voluntary contraction and displays it to help users control movement force. Linearity and measurement accuracy were tested, and the IsoReg's performance was evaluated under typical EEG experimental conditions with 14 participants. The IsoReg demonstrated consistent linearity between applied and measured forces across the required force range, with a mean accuracy of 97% across all participants. The visual force gauge provided normalised force measurements with a mean accuracy exceeding 98.66% across all participants. All participants successfully controlled the motor tasks at the correct relative forces (with a mean accuracy of 89.90%) using the IsoReg, eliminating the impact of inherent force differences between typical WE and WF movements on the EEG analysis. The IsoReg offers a low-cost method for measuring and regulating movements in future neuromuscular studies, potentially leading to improved neural signal interpretation.

Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.

Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments.

Control limitations in the null-space of the wrist muscle system.

The redundancy present within the musculoskeletal system may offer a non-invasive source of signals for movement augmentation, where the set of muscle activations that do not produce force/torque (muscle-to-force null-space) could be controlled simultaneously to the natural limbs. Here, we investigated the viability of extracting movement augmentation control signals from the muscles of the wrist complex. Our study assessed (i) if controlled variation of the muscle activation patterns in the wrist joint's null-space is possible; and (ii) whether force and null-space cursor targets could be reached concurrently. During the null-space target reaching condition, participants used muscle-to-force null-space muscle activation to move their cursor towards a displayed target while minimising the exerted force as visualised through the cursor's size. Initial targets were positioned to require natural co-contraction in the null-space and if participants showed a consistent ability to reach for their current target, they would rotate 5 ∘ incrementally to generate muscle activation patterns further away from their natural co-contraction. In contrast, during the concurrent target reaching condition participants were required to match a target position and size, where their cursor position was instead controlled by their exerted flexion-extension and radial-ulnar deviation, while its size was changed by their natural co-contraction magnitude. The results collected from 10 participants suggest that while there was variation in each participant's co-contraction behaviour, most did not possess the ability to control this variation for muscle-to-force null-space virtual reaching. In contrast, participants did show a direction and target size dependent ability to vary isometric force and co-contraction activity concurrently. Our results indicate the limitations of using the muscle-to-force null-space activity of joints with a low level of redundancy as a possible command signal for movement augmentation.

Sustained submaximal isometric wrist flexion and wrist extension contractions uniquely impair maximal voluntary contraction force in the antagonist wrist action.

When fatigued, the wrist extensors, which are the primary wrist stabilizers, impair distal upper limb motor performance in a surprisingly similar way as when fatiguing the wrist flexors. It is possible that the wrist extensors are so active as antagonists that they develop an equal degree of fatigue during wrist flexion contractions, making it difficult to truly isolate their impact on performance. Thus, the purpose of this study was to examine how wrist flexion/extension forces are impaired following either agonist or antagonist sustained submaximal wrist contractions. 13 male participants attended four laboratory sessions. In these sessions, fatigue was induced via a sustained submaximal isometric contraction of either wrist flexion or extension. These contractions were held for up to 10 min at 20% of the participant's baseline maximal voluntary contraction (MVC) force. Throughout the sustained contraction, intermittent agonist (matching the sustained contraction) or antagonist (opposing the sustained contraction) MVCs were performed. Unsurprisingly, agonist MVC forces decreased significantly more than antagonist (Agonist: 58.5%, Antagonist: 86.5% of MVC, P < 0.001). However, while there were no differences in antagonist wrist extension and flexion MVC decreases (Wrist Flexion: 87.5%, Wrist Extension: 85.5%, P = 0.41), wrist extension MVCs did decrease significantly more than wrist flexion MVCs when forces were expressed relative to the agonist (P = 0.036). These findings partially support the hypothesis that the wrist extensors may be more susceptible to developing fatigue when functioning as antagonists than the wrist flexors. This work will help equip future research into the motor control of the upper limb and the prevention of forearm-related musculoskeletal disorders.

Acute effects of local vibration inducing tonic vibration reflex or illusion of movement on maximal wrist force production.

Local vibration (LV) mainly stimulates primary afferents (Ia) and can induce a tonic vibration reflex (TVR) and an illusion of movement. This study aimed to evaluate the effect of these two phenomena on maximal voluntary isometric contraction (MVIC) capacity. LV (80 Hz) was applied to the wrist flexor muscles in two randomized experiments for 6 min. LV conditions were adjusted to promote either TVR (visual focus on the vibrated wrist) or ILLUSION [hand hidden, visual focus on electromyographic activity of the flexor carpi radialis muscle (FCR)]. Mechanical and electromyographic (EMG) responses of the FCR and extensor carpi radialis muscles were recorded during MVIC in flexion and extension and during electrically evoked contractions at supramaximal intensity. Measurements were performed before (10 min and just before) and after (0 and 30 min) LV protocol. An increase in FCR EMG was observed during LV in the TVR condition (+340%) compared with the illusion condition (P = 0.003). In contrast, the movement illusion was greater in the ILLUSION condition (assessed through subjective scales) (P = 0.004). MVIC was reduced in flexion only after the TVR condition ([Formula: see text], all P < 0.034). Moreover, the decrease in force was correlated with the amount of TVR recorded on the FCR muscle (r = -0.64, P = 0.005). Although potentiated doublets of each muscle did not evolve differently between conditions, a decrease was observed between the first and the last measure. In conclusion, when conducting research to assess maximal strength, it is necessary to have better control and reporting of the phenomena induced during LV.NEW & NOTEWORTHY The maximal force production of the vibrated muscle is reduced after 6 min of LV only in TVR condition. Furthermore, the amount of TVR is negatively correlated with this force decrease. When measuring the effects of LV on maximal force production, it is important to control and report any phenomena induced during vibration, such as TVR or movement illusion, which can be achieved by recording EMG activity of vibrated muscle and quantifying illusion.

A data-driven approach to detect upper limb functional use during daily life in breast cancer survivors using wrist-worn sensors.

To gain insights into the impact of upper limb (UL) dysfunctions after breast cancer treatment, this study aimed to develop a temporal convolutional neural network (TCN) to detect functional daily UL use in breast cancer survivors using data from a wrist-worn accelerometer. A pre-existing dataset of 10 breast cancer survivors was used that contained raw 3-axis acceleration data and simultaneously recorded video data, captured during four daily life activities. The input of our TCN consists of a 3-axis acceleration sequence with a receptive field of 243 samples. The 4 ResNet TCN blocks perform dilated temporal convolutions with a kernel of size 3 and a dilation rate that increases by a factor of 3 after each iteration. Outcomes of interest were functional UL use (minutes) and percentage UL use. We found strong agreement between the video and predicted data for functional UL use (ICC = 0.975) and moderately strong agreement for %UL use (ICC = 0.794). The TCN model overestimated the functional UL use by 0.71 min and 3.06%. Model performance showed good accuracy, f1, and AUPRC scores (0.875, 0.909, 0.954, respectively). In conclusion, using wrist-worn accelerometer data, the TCN model effectively identified functional UL use in daily life among breast cancer survivors.

Tai Chi Expertise Classification in Older Adults Using Wrist Wearables and Machine Learning.

Tai Chi is a Chinese martial art that provides an adaptive and accessible exercise for older adults with varying functional capacity. While Tai Chi is widely recommended for its physical benefits, wider adoption in at-home practice presents challenges for practitioners, as limited feedback may hamper learning. This study examined the feasibility of using a wearable sensor, combined with machine learning (ML) approaches, to automatically and objectively classify Tai Chi expertise. We hypothesized that the combination of wrist acceleration profiles with ML approaches would be able to accurately classify practitioners' Tai Chi expertise levels. Twelve older active Tai Chi practitioners were recruited for this study. The self-reported lifetime practice hours were used to identify subjects in low, medium, or highly experienced groups. Using 15 acceleration-derived features from a wearable sensor during a self-guided Tai Chi movement and 8 ML architectures, we found multiclass classification performance to range from 0.73 to 0.97 in accuracy and F1-score. Based on feature importance analysis, the top three features were found to each result in a 16-19% performance drop in accuracy. These findings suggest that wrist-wearable-based ML models may accurately classify practice-related changes in movement patterns, which may be helpful in quantifying progress in at-home exercises.

Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs.

Human-Machine Interfaces (HMIs) have gained popularity as they allow for an effortless and natural interaction between the user and the machine by processing information gathered from a single or multiple sensing modalities and transcribing user intentions to the desired actions. Their operability depends on frequent periodic re-calibration using newly acquired data due to their adaptation needs in dynamic environments, where test-time data continuously change in unforeseen ways, a cause that significantly contributes to their abandonment and remains unexplored by the Ultrasound-based (US-based) HMI community. In this work, we conduct a thorough investigation of Unsupervised Domain Adaptation (UDA) algorithms for the re-calibration of US-based HMIs during within-day sessions, which utilize unlabeled data for re-calibration. Our experimentation led us to the proposal of a CNN-based architecture for simultaneous wrist rotation angle and finger gesture prediction that achieves comparable performance with the state-of-the-art while featuring 87.92% less trainable parameters. According to our findings, DANN (a Domain-Adversarial training algorithm), with proper initialization, offers an average 24.99% classification accuracy performance enhancement when compared to no re-calibration setting. However, our results suggest that in cases where the experimental setup and the UDA configuration may differ, observed enhancements would be rather small or even unnoticeable.

Unusual and very rare presentation of wrist pain in a man with acute anterior myocardial infarction, a case report and literature of review.

BACKGROUND: As acute myocardial infarction (AMI) prevalence is increasing because of lifestyle changes, the incidence of atypical symptoms in acute coronary syndrome (ACS) is rising and making misdiagnosing of this fatal event more probable. To better approach the patients with atypical symptoms, we tend to present a rare case of AMI with wrist pain. CASE REPORT: A 41-year-old man presented to the emergency room (ER) with severe both-hand wrist pain and mild epigastric pain. His electrocardiogram (ECG) showed anterior ST-elevation myocardial infarction (MI) with an ejection fraction of 35-40%. His angiography showed severe left anterior descending artery (LAD), and first obtuse marginal artery (OM1) artery stenosis. He underwent Primary percutaneous coronary intervention (PCI). The patient recovered without serious complications and was discharged the day after PCI. DISCUSSION: In this rare case of AMI with wrist pain, it is important to know that atypical symptoms can be present at various levels of symptoms, which prevents future misdiagnosis.

A compact solution for vibrotactile proprioceptive feedback of wrist rotation and hand aperture.

BACKGROUND: Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. METHODS: We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. RESULTS: Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. CONCLUSIONS: The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Describing the Flick Movement Kinematic Biomechanically and Investigating the Effect of Thrower's Ten Exercises in Underwater Hockey Players.

CONTEXT: Analyzing flick movement kinematics biomechanically is important to prevent sport-related injuries in underwater hockey players since the aquatic-based flick movement is completely different from land-based flick movements. The study aimed to describe the flick movement kinematic biomechanically in underwater hockey players. Moreover, this study further aimed to investigate the effect of the Thrower's Ten exercises on flick movement kinematics in underwater hockey players. DESIGN: Descriptive laboratory study. METHODS: Seventeen underwater hockey players (age: 26.2 [4.3] y; sports age: 6.2 [4.5] y) were included. First, 2 underwater cameras using motion capture video analysis MATLAB were used to biomechanically analyze the angular changes on the shoulder, elbow, wrist, and body while players were performing the flick movements. Players were then recruited to the Thrower's Ten exercise program for 6 weeks. Flick movement kinematics and flick-throwing distance were recorded at baseline and 6 weeks. RESULTS: The flick movement kinematic patterns demonstrated increased shoulder flexion (from 102.5° to 144.9°), wrist extension (from 9.5° to 10.8°), and upper-extremity rotation (from 5.7° to 56.8°) while decreased elbow extension (from 107.7° to 159.2°) from the stick met the puck until the competition of the movement. The Thrower's Ten exercises improved the elbow extension (P = .04), wrist extension (P = .01), body rotation (P < .001), and flick-throwing distance (P < .001) from baseline to 6 weeks. CONCLUSION: This study describes the underwater flick kinematic technique biomechanically and interprets preliminary findings for the first time. Thus, 6 weeks of Thrower's Ten exercise program provides more body muscle movements than the smaller ones during the flick movements and higher flick-throwing distance in underwater hockey players.

Effects of Different Test Positions on Quantitative Muscle Strength of Wrist and Finger Flexor Muscle Groups and Its Standardization.

OBJECTIVES: To explore the effects of different test positions on quantitative muscle strength of wrist and finger flexor muscle groups and to establish a standardized muscle strength test protocol for each muscle group. METHODS: Forty healthy subjects (12 males and 28 females) were recruited. A portable digital quantitative muscle strength tester, Micro FET2TM, was used to measure the flexor muscle strength of each finger and the wrist joint at the 30° extension, 0° neutral, and 30° flexion, respectively. Palmar abduction strength of the thumb was measured at 30° and 60°, respectively. Ten subjects were randomly selected from the 40 subjects, and the quantitative muscle strength of each muscle group was tested again by the same operator after an interval of 10 to 15 days. RESULTS: Except for the fact that in males, there was no significant difference in flexor muscle strength of thumb and wrist joint between 30° of wrist extension and neutral 0° position, the muscle strength of the other fingers flexion and wrist palmar flexor showed the following characteristics：30° of wrist extension > neutral 0° position > 30° of flexion, and the PAST was 30°>60°; The flexor muscle strength of all the subjects was thumb > index finger > middle finger > ring finger > little finger; All muscle strength values of male were greater than those of female, and the difference was statistically significant (P<0.05); There was no significant difference between the left and right side muscle strength values of all subjects (P>0.05). The reliability of muscle strength values measured at different times in 10 subjects was good. CONCLUSIONS: The quantitative muscle strength of each muscle group of the hand and wrist is affected by the test position, and a standardized and uniformed test position should be adopted in the actual identification. Micro FET2TM has good reliability for hand and wrist quantitative muscle strength testing. The 30° extension of the wrist can be used as the best standardized test position for the flexion muscle strength of each finger and wrist joint. The 30° position can be used as the best standardized test position for PAST.

Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset.

Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. While X-ray imaging remains a prevalent diagnostic tool, the increasing misinterpretation rates highlight the need for more accurate analysis, especially considering the lack of specialized training among many surgeons and physicians. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. However, distinguishing subtle variations between pediatric wrist pathologies in X-rays remains challenging. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. In this paper, we address the challenge of pediatric wrist pathology recognition with a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention. We refine our fine-grained architecture through ablation analysis and the integration of LION. Leveraging Grad-CAM, an explainable AI technique, we highlight these regions. Despite using limited data, reflective of real-world medical study constraints, our method consistently outperforms state-of-the-art image recognition models on both augmented and original (challenging) test sets. Our proposed refined architecture achieves an increase in accuracy of 1.06% and 1.25% compared to the baseline method, resulting in accuracies of 86% and 84%, respectively. Moreover, our approach demonstrates the highest fracture sensitivity of 97%, highlighting its potential to enhance wrist pathology recognition.

Daily-Life Walking Speed, Quality and Quantity Derived from a Wrist Motion Sensor: Large-Scale Normative Data for Middle-Aged and Older Adults.

Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45-79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test-retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test-retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications.

Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices.

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.

Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks.

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75-0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.

Antihypertensive effect of esaxerenone and correlation between brachial and wrist home monitoring devices in patients with nocturnal hypertension: A post hoc analysis of the EARLY-NH study.

Adequate management of nocturnal hypertension is crucial to reduce the risk of organ damage and cardiovascular events. The EARLY-NH study was a prospective, open-label, multicenter study conducted in Japanese patients with nocturnal hypertension who received esaxerenone treatment for 12 weeks. This post hoc analysis aimed to assess (1) the relationship between changes in morning home systolic blood pressure (SBP), bedtime home SBP, and nighttime home SBP based on changes in SBP and achievement rates of target SBP levels; and (2) the correlation between nighttime home SBP measurements using brachial and wrist home BP monitoring (HBPM) devices. This analysis evaluated 82 patients who completed the 12-week treatment period. Among those who achieved target morning home SBP (<135 mmHg) and target bedtime home SBP (<135 mmHg), the brachial HBPM device showed achievement rates of 63.6% and 56.4%, respectively, for target nighttime home SBP (<120 mmHg). The wrist device showed achievement rates of 66.7% and 63.4%, respectively, for the same targets. Significant correlations were observed between both devices for nighttime home SBP measurements at baseline (r = 0.790), Week 12 (r = 0.641), and change from baseline to Week 12 (r = 0.533) (all, p < .001). In this patient population, approximately 60% of individuals who reached target morning or bedtime home SBP levels <135 mmHg exhibited well-controlled nighttime home SBP. Although nighttime home SBP measurements obtained using both brachial and wrist HBPM devices displayed a significant correlation, the wrist device needs to be examined in more detail for clinical use.