RESUMO
The severe mismatch between solid bioelectronics and dynamic biological tissues has posed enduring challenges in the biomonitoring community. Here, we developed a reconfigurable liquid cardiac sensor capable of adapting to dynamic biological tissues, facilitating ambulatory cardiac monitoring unhindered by motion artifacts or interference from other biological activities. We employed an ultrahigh-resolution 3D scanning technique to capture tomographic images of the skin on the wrist. Then, we established a theoretical model to gain a deep understanding of the intricate interaction between our reconfigurable sensor and dynamic biological tissues. To properly elucidate the advantages of this sensor, we conducted cardiac monitoring alongside benchmarks such as the electrocardiogram. The liquid cardiac sensor was demonstrated to produce stable signals of high quality (23.1 dB) in ambulatory settings.
Assuntos
Monitorização Ambulatorial , Humanos , Monitorização Ambulatorial/instrumentação , Monitorização Ambulatorial/métodos , Eletrocardiografia/instrumentação , Eletrocardiografia/métodos , Desenho de Equipamento , Punho , Técnicas Biossensoriais/instrumentação , Técnicas Biossensoriais/métodos , Pele , Dispositivos Eletrônicos Vestíveis , Imageamento Tridimensional/instrumentação , Coração/fisiologia , Coração/diagnóstico por imagemRESUMO
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.
Assuntos
Acelerometria , Marcha , Aprendizado de Máquina Supervisionado , Humanos , Idoso , Masculino , Feminino , Marcha/fisiologia , Acelerometria/métodos , Acelerometria/instrumentação , Idoso de 80 Anos ou mais , Atividades Cotidianas , Punho , Algoritmos , Dispositivos Eletrônicos Vestíveis , Pessoa de Meia-IdadeRESUMO
Hand choice is an unconscious decision frequently made in daily life. The electroencephalogram before target presentation correlates with hand choice for the target where hand choice probability reaches equilibrium. However, whether neurophysiological interventions before target presentation influence hand choice remains unknown. Therefore, this study determined whether instantaneous somatosensory electrical stimulation administered to the unilateral wrist at 0, 300, or 600 ms before the target presentation facilitates or inhibits stimulated hand choice for targets around the hand selection equilibrium point. A single electrical stimulation comprised five trains of 1 ms electrical pulses, with a 20 ms inter-pulse interval. The stimulus intensity was set at 80% of the motor threshold. This study included 14 right-handed healthy adults (five females and nine males; mean age, 25.1 ± 4.64 years). Unilateral wrist stimulation significantly increased the probability of choosing the stimulated hand and led to a faster reaction time than bilateral wrist stimulation and no-stimulation conditions. The results suggest that prior somatosensory stimulation significantly affects the hand-choice process, effectively promoting the selection of the stimulated hand. These findings highlight the potential application of this stimulation method in stroke rehabilitation to facilitate the use of the paretic hand.
Assuntos
Comportamento de Escolha , Estimulação Elétrica , Mãos , Punho , Humanos , Feminino , Masculino , Punho/fisiologia , Adulto , Mãos/fisiologia , Estimulação Elétrica/métodos , Comportamento de Escolha/fisiologia , Adulto Jovem , Tempo de Reação/fisiologia , EletroencefalografiaRESUMO
BACKGROUND: Wrist-worn data from commercially available devices has potential to characterize sedentary time for research and for clinical and public health applications. We propose a model that utilizes heart rate in addition to step count data to estimate the proportion of time spent being sedentary and the usual length of sedentary bouts. METHODS: We developed and trained two Hidden semi-Markov models, STEPHEN (STEP and Heart ENcoder) and STEPCODE (STEP enCODEr; a steps-only based model) using consumer-grade Fitbit device data from participants under free living conditions, and validated model performance using two external datasets. We used the median absolute percentage error (MDAPE) to measure the accuracy of the proposed models against research-grade activPAL device data as the referent. Bland-Altman plots summarized the individual-level agreement with activPAL. RESULTS: In OPTIMISE cohort, STEPHEN's estimates of the proportion of time spent sedentary had significantly (p < 0.001) better accuracy (MDAPE [IQR] = 0.15 [0.06-0.25] vs. 0.23 [0.13-0.53)]) and agreement (Bias Mean [SD]=-0.03[0.11] vs. 0.14 [0.11]) than the proprietary software, estimated the usual sedentary bout duration more accurately (MDAPE[IQR] = 0.11[0.06-0.26] vs. 0.42[0.32-0.48]), and had better agreement (Bias Mean [SD] = 3.91[5.67] minutes vs. -11.93[5.07] minutes). With the ALLO-Active dataset, STEPHEN and STEPCODE did not improve the estimation of proportion of time spent sedentary, but STEPHEN estimated usual sedentary bout duration more accurately than the proprietary software (MDAPE[IQR] = 0.19[0.03-0.25] vs. 0.36[0.15-0.48]) and had smaller bias (Bias Mean[SD] = 0.70[8.89] minutes vs. -11.35[9.17] minutes). CONCLUSIONS: STEPHEN can characterize the proportion of time spent being sedentary and usual sedentary bout length. The methodology is available as an open access R package available from https://github.com/limfuxing/stephen/ . The package includes trained models, but users have the flexibility to train their own models.
Assuntos
Comportamento Sedentário , Punho , Humanos , Masculino , Feminino , Adulto , Acelerometria/instrumentação , Acelerometria/métodos , Frequência Cardíaca/fisiologia , Dispositivos Eletrônicos Vestíveis/estatística & dados numéricos , Exercício Físico/fisiologia , Pessoa de Meia-Idade , Monitores de Aptidão Física/estatística & dados numéricos , Monitores de Aptidão Física/normas , Fatores de Tempo , Adulto JovemRESUMO
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.
Assuntos
Eletromiografia , Redes Neurais de Computação , Articulação do Punho , Humanos , Eletromiografia/métodos , Articulação do Punho/fisiologia , Amplitude de Movimento Articular/fisiologia , Movimento/fisiologia , Processamento de Sinais Assistido por Computador , Algoritmos , Adulto , Masculino , Punho/fisiologiaRESUMO
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.
Assuntos
Postura , Dispositivos Eletrônicos Vestíveis , Humanos , Postura/fisiologia , Doenças Musculoesqueléticas/fisiopatologia , Agricultura/métodos , Agricultura/instrumentação , Punho/fisiologia , Fenômenos Biomecânicos/fisiologia , Adulto , Masculino , Técnicas Biossensoriais/instrumentação , Técnicas Biossensoriais/métodos , Movimento/fisiologiaRESUMO
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.
Assuntos
Eletroencefalografia , Punho , Humanos , Eletroencefalografia/métodos , Punho/fisiologia , Masculino , Adulto , Feminino , Movimento/fisiologia , Interfaces Cérebro-Computador , Adulto Jovem , Dinamômetro de Força Muscular , Amplitude de Movimento Articular/fisiologiaRESUMO
The aim of this contribution is to present a segmentation method for the identification of voluntary movements from inertial data acquired through a single inertial measurement unit placed on the subject's wrist. Inertial data were recorded from 25 healthy subjects while performing 75 consecutive reach-to-grasp movements. The approach herein presented, called DynAMoS, is based on an adaptive thresholding step on the angular velocity norm, followed by a statistics-based post-processing on the movement duration distribution. Post-processing aims at reducing the number of erroneous transitions in the movement segmentation. We assessed the segmentation quality of this method using a stereophotogrammetric system as the gold standard. Two popular methods already presented in the literature were compared to DynAMoS in terms of the number of movements identified, onset and offset mean absolute errors, and movement duration. Moreover, we analyzed the sub-phase durations of the drinking movement to further characterize the task. The results show that the proposed method performs significantly better than the two state-of-the-art approaches (i.e., percentage of erroneous movements = 3%; onset and offset mean absolute error < 0.08 s), suggesting that DynAMoS could make more effective home monitoring applications for assessing the motion improvements of patients following domicile rehabilitation protocols.
Assuntos
Força da Mão , Movimento , Humanos , Masculino , Adulto , Feminino , Movimento/fisiologia , Força da Mão/fisiologia , Algoritmos , Fenômenos Biomecânicos/fisiologia , Punho/fisiologia , Adulto JovemRESUMO
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.
Assuntos
Terapia por Estimulação Elétrica , Doença de Parkinson , Tremor , Humanos , Masculino , Feminino , Pessoa de Meia-Idade , Tremor/terapia , Tremor/fisiopatologia , Idoso , Doença de Parkinson/terapia , Doença de Parkinson/fisiopatologia , Terapia por Estimulação Elétrica/métodos , Tremor Essencial/terapia , Tremor Essencial/fisiopatologia , Punho , Resultado do TratamentoRESUMO
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.
Assuntos
Contração Muscular , Músculo Esquelético , Articulação do Punho , Punho , Humanos , Músculo Esquelético/fisiologia , Masculino , Feminino , Punho/fisiologia , Adulto , Articulação do Punho/fisiologia , Contração Muscular/fisiologia , Eletromiografia , Movimento/fisiologia , Adulto Jovem , Fenômenos BiomecânicosRESUMO
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.
Assuntos
Membros Artificiais , Retroalimentação Sensorial , Mãos , Propriocepção , Vibração , Punho , Humanos , Retroalimentação Sensorial/fisiologia , Propriocepção/fisiologia , Adulto , Masculino , Punho/fisiologia , Feminino , Mãos/fisiologia , Amputados/reabilitação , Rotação , Adulto Jovem , Pessoa de Meia-Idade , Tato/fisiologiaRESUMO
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.
Assuntos
Contração Isométrica , Fadiga Muscular , Músculo Esquelético , Punho , Humanos , Masculino , Contração Isométrica/fisiologia , Fadiga Muscular/fisiologia , Adulto , Adulto Jovem , Punho/fisiologia , Músculo Esquelético/fisiologia , Articulação do Punho/fisiologia , Amplitude de Movimento Articular/fisiologia , Fenômenos Biomecânicos/fisiologia , EletromiografiaRESUMO
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.
Assuntos
Eletromiografia , Ilusões , Contração Isométrica , Movimento , Músculo Esquelético , Reflexo , Vibração , Punho , Humanos , Masculino , Punho/fisiologia , Adulto , Músculo Esquelético/fisiologia , Reflexo/fisiologia , Contração Isométrica/fisiologia , Eletromiografia/métodos , Ilusões/fisiologia , Movimento/fisiologia , Feminino , Adulto JovemRESUMO
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.
Assuntos
Punho , Humanos , Criança , Adolescente , Punho/diagnóstico por imagem , Traumatismos do Punho/diagnóstico por imagem , Masculino , Feminino , Fraturas Ósseas/diagnóstico por imagem , Redes Neurais de Computação , Pré-EscolarRESUMO
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.
Assuntos
Acelerometria , Atividades Cotidianas , Neoplasias da Mama , Sobreviventes de Câncer , Extremidade Superior , Dispositivos Eletrônicos Vestíveis , Punho , Humanos , Feminino , Extremidade Superior/fisiopatologia , Pessoa de Meia-Idade , Acelerometria/instrumentação , Redes Neurais de Computação , Adulto , IdosoRESUMO
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.
Assuntos
Aprendizado de Máquina , Tai Chi Chuan , Dispositivos Eletrônicos Vestíveis , Punho , Humanos , Tai Chi Chuan/métodos , Idoso , Punho/fisiologia , Masculino , Feminino , Pessoa de Meia-IdadeRESUMO
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.
Assuntos
Algoritmos , Ultrassonografia , Humanos , Ultrassonografia/métodos , Interface Usuário-Computador , Punho/fisiologia , Punho/diagnóstico por imagem , Redes Neurais de Computação , Dedos/fisiologia , Sistemas Homem-Máquina , GestosRESUMO
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.
Assuntos
Velocidade de Caminhada , Punho , Humanos , Masculino , Feminino , Idoso , Pessoa de Meia-Idade , Punho/fisiologia , Velocidade de Caminhada/fisiologia , Caminhada/fisiologia , Marcha/fisiologia , Dispositivos Eletrônicos Vestíveis , Qualidade de Vida , Reprodutibilidade dos TestesRESUMO
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.
Assuntos
Fontes de Energia Elétrica , Dispositivos Eletrônicos Vestíveis , Punho , Humanos , Punho/fisiologia , Cabeça/fisiologia , Desenho de Equipamento , Monitorização Fisiológica/instrumentação , Monitorização Fisiológica/métodosRESUMO
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.