Wearable Machine Research Articles

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

Analysis of waist and wrist positioning wearable machine learning models to detect falls

AbstractFalls have a global impact, affecting people worldwide, with a notably high occurrence among the elderly. This study employs machine learning techniques to analyze falls and simulate Activities of Daily Living (ADL). The objective is to predict human falls by leveraging signals from accelerometers and gyroscopes as wearable sensors. By deriving statistical features such as mean, standard deviation, and range the authors successfully trained and assessed six machine learning models allowing them to compare solutions based on both wrist and waist data. The combination of these characteristics and sensors resulted in the Random Forest waist model achieving the most favorable metrics, with an accuracy rate of 97.22% in a 5‐s window.

Stretchable Gold Nanomembrane Electrode with Ionic Hydrogel Skin-Adhesive Properties.

Skin has a dynamic surface and offers essential information through biological signals originating from internal organs, blood vessels, and muscles. Soft and stretchable bioelectronics can be used in wearable machines for long-term stability and to continuously obtain distinct bio-signals in conjunction with repeated expansion and contraction with physical activities. While monitoring bio-signals, the electrode and skin must be firmly attached for high signal quality. Furthermore, the signal-to-noise ratio (SNR) should be high enough, and accordingly, the ionic conductivity of an adhesive hydrogel needs to be improved. Here, we used a chitosan-alginate-chitosan (CAC) triple hydrogel layer as an interface between the electrodes and the skin to enhance ionic conductivity and skin adhesiveness and to minimize the mechanical mismatch. For development, thermoplastic elastomer Styrene-Ethylene-Butylene-Styrene (SEBS) dissolved in toluene was used as a substrate, and gold nanomembranes were thermally evaporated on SEBS. Subsequently, CAC triple layers were drop-casted onto the gold surface one by one and dried successively. Lastly, to demonstrate the performance of our electrodes, a human electrocardiogram signal was monitored. The electrodes coupled with our CAC triple hydrogel layer showed high SNR with clear PQRST peaks.

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review.

Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants' physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants' physical activity evolves. The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.

Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements.

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

Sustainable Compliant Physical Interaction in a Biped-Wheeled Wearable Machine

This paper deals with the problem of the physical human-machine interaction in biped-wheeled exoskeletons and underlines how the symbiosis between humans and machines may increase sustainability. Few exoskeletons in the world are designed with wheels, but the evolution of wearable machines in industries and the convenience of using wheels, underline the importance of the novel research sector of biped-wheeled exoskeletons. This paper shows the functional design and simulation of a novel biped-wheeled wearable machine, including sustainable compliant physical interaction with the subject on board. In particular, the multibody model of the proposed machine is studied and simulated with the subject model on board, including human-machine compliant interactions. The classical human walking cycle is implemented in the machine, varying the speed and the joint compliance of the subject on board and comparing the torque and power output of the motors of the biped-wheeled exoskeleton. The results of this study underline how the joint compliance of the subject on board of the biped-wheeled exoskeleton may influence the efficiency and sustainability of the biped-wheeled wearable machine.

Bio-signals Compression using Auto-Encoder

Latest developments in wearable machines permits un-damageable and cheapest way for gathering of medical data such as bio-signals (Electro-Cardio-Gram), Respiration, Blood pressure and so on. Gathering and processing of different biomarkers are considered to provide anticipatory healthcare by personalized software for medical purpose. As the wearable machines are dependent on size of the machine, different conditional resources and are used by batteries, we need to perform small algorithms to robustly control memory and the energy of the device (Machine). The rapid growth of the technology has led to various auto encoders that guarantee the results by extracting feature selection from time, frequency, and time-frequency domain in an efficient way. The main aim was to train hidden layer in a way, which can reconstruct the compressed data almost similar to the input data. While in the previous works, while compressing the data all the features were needed necessarily in the hidden layer to reconstruct it back. But, in our proposed work, a new algorithm BCAE Bio-signals Compression using Auto-Encoder is used, which is a re-construction model that performs selection of the important features and compression of the data using auto-encoder. The core idea is to perform combined feature selection and compression for ECG signals at source and to decrease energy consumption while transmitting and thus prolongs the battery lifetime. Fitness monitors for wearables by using new methodologies will differentiate the unwanted features from the necessary once for further classification. Performance results produced with respect to ECG signals of our BCAE computations from viable wearable monitor that is wireless with same of Subject-Adaptive-Unsupervised (SURF) ECG compression for devices that are wearable represents the dominance of BCAE when compared with the SURF ECG signal compression for Fitness-Monitors-of-Wearable devices (SURF). 1) Ratio of compression is around 90x ; 2) error of re-construction ranging between 2% - 7% and 3) minimization of power usage of around 2 orders of magnitude based on transmitting the uncompressed signals during maintaining its morphology.

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Human dynamics: computation for organizations

The human dynamics group at the MIT Media Laboratory proposes that active pattern analysis of face-to-face interactions within the workplace can radically improve the functioning of the organization. There are several different types of information inherent in such conversations: interaction features, participants, context, and content. By aggregating this information, high-potential collaborations and expertise within the organization can be identified, and information efficiently distributed. Examples of using wearable machine perception to characterize face-to-face interactions and using the results to initiate productive connections are described, and privacy concerns are addressed.

「ウェアラブルな機械の時代/人と機械のインタラクション」小特集号発刊に際して(<小特集>ウェアラブルな機械の時代/人と機械のインタラクション)

「ウェアラブルな機械の時代/人と機械のインタラクション」小特集号発刊に際して(<小特集>ウェアラブルな機械の時代/人と機械のインタラクション)