Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults.

INTRODUCTION: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. METHODS: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. RESULTS: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). CONCLUSIONS: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables.

Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

Utilising dynamic motor control index to identify age-related differences in neuromuscular control.

PURPOSE: Considering the relationship between aging and neuromuscular control decline, early detection of age-related changes can ensure that timely interventions are implemented to attenuate or restore neuromuscular deficits. The dynamic motor control index (DMCI), a measure based on variance accounted for (VAF) by one muscle synergy (MS), is a metric used to assess age-related changes in neuromuscular control. The aim of the study was to investigate the use of one-synergy VAF, and consecutively DMCI, in assessing age-related changes in neuromuscular control over a range of exercises with varying difficulty. METHODS: Thirty-one subjects walked on a flat and inclined treadmill, as well as performed forward and lateral stepping up tasks. Motion and muscular activity were recorded, and muscle synergy analysis was conducted using one-synergy VAF, DMCI, and number of synergies. RESULTS: Difference between older and younger group was observed for one-synergy VAF, DMCI for forward stepping up task (one-synergy VAF difference of 2.45 (0.22, 4.68) and DMCI of 9.21 (0.81, 17.61), p = 0.033), but not for lateral stepping up or walking. CONCLUSION: The use of VAF based metrics and specifically DMCI, rather than number of MS, in combination with stepping forward exercise can provide a low-cost and easy to implement approach for assessing neuromuscular control in clinical settings.

The Potential of Electrospinning to Enable the Realization of Energy-Autonomous Wearable Sensing Systems.

The market for wearable electronic devices is experiencing significant growth and increasing potential for the future. Researchers worldwide are actively working to improve these devices, particularly in developing wearable electronics with balanced functionality and wearability for commercialization. Electrospinning, a technology that creates nano/microfiber-based membranes with high surface area, porosity, and favorable mechanical properties for human in vitro and in vivo applications using a broad range of materials, is proving to be a promising approach. Wearable electronic devices can use mechanical, thermal, evaporative and solar energy harvesting technologies to generate power for future energy needs, providing more options than traditional sources. This review offers a comprehensive analysis of how electrospinning technology can be used in energy-autonomous wearable wireless sensing systems. It provides an overview of the electrospinning technology, fundamental mechanisms, and applications in energy scavenging, human physiological signal sensing, energy storage, and antenna for data transmission. The review discusses combining wearable electronic technology and textile engineering to create superior wearable devices and increase future collaboration opportunities. Additionally, the challenges related to conducting appropriate testing for market-ready products using these devices are also discussed.

Acoustic emissions and age-related changes of the knee.

Acoustic emission (AE) monitoring is currently being widely investigated as a diagnostic tool in orthopedics, in particular for osteoarthritis (OA) diagnostics. Considering that age is one of the main risk factors for OA, investigating age-related changes in joint AEs might provide an additional incentive for further studies and consequent translation to clinical practice. The aim of this study is to investigate age-related changes in knee AE and determine AE hit definition modes as well as AE hit parameters that allow for improved age group differentiation. Knee AEs were recorded from 51 participants in two age groups (18-35 and 50-75 years old) whilst cycling with 30 and 60 rpm cadence. Two AE sensors with 15-40 kHz and 100-450 kHz frequency ranges were used, and three AE event detection modes investigated. Additionally, participants' Knee Osteoarthritis Outcome Scores (KOOS) were recorded. Low frequency sensors (15-40kHz) and hit modes with shortened hit and peak definition times showed the potential to distinguish between age groups. Moreover, a weak correlation was found between only three parameters (AE event median duration, rise time, and signal strength) and age, indicating that changes in joint AE are most likely associated with pathological changes rather than physiological ageing within the healthy norm.Clinical Relevance- the use of AE monitoring was examined in the context of age-related changes in knee health. The study indicates the potential for knee AE monitoring to be used as a quantitative measure of pathological changes in the knee status.

Development of a PPG-based hardware and software system deployable on elbow and thumb for real-time estimation of pulse transit time.

Blood pressure (BP) is a vital parameter used by clinicians to diagnose issues in the human cardiovascular system. Cuff-based BP devices are currently the standard method for on-the-spot and ambulatory BP measurements. However, cuff-based devices are not comfortable and are not suitable for long-term BP monitoring. Many studies have reported a significant correlation between pulse transit time (PTT) with blood pressure. However, this relation is impacted by many internal and external factors which might lower the accuracy of the PTT method. In this paper, we present a novel hardware system consisting of two custom photoplethysmography (PPG) sensors designed particularly for the estimation of PTT. In addition, a software interface and algorithms have been implemented to perform a real-time assessment of the PTT and other features of interest from signals gathered between the brachial artery and the thumb. A preclinical study has been conducted to validate the system. Five healthy volunteer subjects were tested and the results were then compared with those gathered using a reference device. The analysis reports a mean difference among subjects equal to -3.75±7.28 ms. Moreover, the standard deviation values obtained for each individual showed comparable results with the reference device, proving to be a valuable tool to investigate the factors impacting the BP-PTT relationship.Clinical Relevance- The proposed system proved to be a feasible solution to detect blood volume changes providing good quality signals to be used in the study of BP-PTT relationship.

Validation of Endurance Model for Manual Tasks.

Physical fatigue in the workplace can lead to work-related musculoskeletal disorders (WMSDs), especially in occupations that require repetitive, mid-air movements, such as manufacturing and assembly tasks in industry settings. The current paper endeavors to validate an existing torque-based fatigue prediction model for lifting tasks. The model uses anthropometrics and the maximum torque of the individual to predict the time to fatigue. Twelve participants took part in the study which measured body composition parameters and the maximum force produced by the shoulder joint in flexion, followed by three lifting tasks for the shoulder in flexion, including isometric and dynamic tasks with one and two hands. Inertial measurements units (IMUs) were worn by participants to determine the torque at each instant to calculate the endurance time and CE, while a self-subjective questionnaire was utilized to assess physical exertion, the Borg Rate of Perceived Exertion (RPE) scale. The model was effective for static and two-handed tasks and produced errors in the range of [28.62 49.21] for the last task completed, indicating the previous workloads affect the endurance time, even though the individual perceives they are fully rested. The model was not effective for the one-handed dynamic task and differences were observed between males and females, which will be the focus of future work.An individualized, torque-based fatigue prediction model, such as the model presented, can be used to design worker-specific target levels and workloads, take inter and intra individual differences into account, and put fatigue mitigating interventions into place before fatigue occurs; resulting in potentially preventing WMSDs, aiding in worker wellbeing and benefitting the quality and efficiency of the work output.Clinical Relevance- This research provides the basis for an individualized, torque-based approach to the prediction of fatigue at the shoulder joint which can be used to assign worker tasks and rest breaks, design worker specific targets and reduce the prevalence of work-related musculoskeletal disorders in occupational settings.

Machine learning based canine posture estimation using inertial data.

The aim of this study was to design a new canine posture estimation system specifically for working dogs. The system was composed of Inertial Measurement Units (IMUs) that are commercially available, and a supervised learning algorithm which was developed for different behaviours. Three IMUs, each containing a 3-axis accelerometer, gyroscope, and magnetometer, were attached to the dogs' chest, back, and neck. To build and test the model, data were collected during a video-recorded behaviour test where the trainee assistance dogs performed static postures (standing, sitting, lying down) and dynamic activities (walking, body shake). Advanced feature extraction techniques were employed for the first time in this field, including statistical, temporal, and spectral methods. The most important features for posture prediction were chosen using Select K Best with ANOVA F-value. The individual contributions of each IMU, sensor, and feature type were analysed using Select K Best scores and Random Forest feature importance. Results showed that the back and chest IMUs were more important than the neck IMU, and the accelerometers were more important than the gyroscopes. The addition of IMUs to the chest and back of dog harnesses is recommended to improve performance. Additionally, statistical and temporal feature domains were more important than spectral feature domains. Three novel cascade arrangements of Random Forest and Isolation Forest were fitted to the dataset. The best classifier achieved an f1-macro of 0.83 and an f1-weighted of 0.90 for the prediction of the five postures, demonstrating a better performance than previous studies. These results were attributed to the data collection methodology (number of subjects and observations, multiple IMUs, use of common working dog breeds) and novel machine learning techniques (advanced feature extraction, feature selection and modelling arrangements) employed. The dataset and code used are publicly available on Mendeley Data and GitHub, respectively.

Non-Invasive Assessment of Cartilage Damage of the Human Knee Using Acoustic Emission Monitoring: A Pilot Cadaver Study.

OBJECTIVE: Knee osteoarthritis is currently one of the top causes of disability in older population, a rate that will only increase in the future due to an aging population and the prevalence of obesity. However, objective assessment of treatment outcomes and remote evaluation are still in need of further development. Acoustic emission (AE) monitoring in knee diagnostics has been successfully adopted in the past; however, a wide discrepancy among the adopted AE techniques and analyses exists. This pilot study determined the most suitable metrics to differentiate progressive cartilage damage and the optimal frequency range and placement of AE sensors. METHODS: Knee AEs were recorded in the 100-450 kHz and 15-200kH frequency ranges from a cadaver specimen in knee flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. RESULTS: AE events in the lower frequency range and the following parameters provided better distinction between intact and damaged knee: hit amplitude, signal strength, and absolute energy. The medial condyle area of the knee was less prone to artefacts and unsystematic noise. Multiple reopenings of the knee compartment in the process of introducing the damage negatively affected the quality of the measurements. CONCLUSION: Results may improve AE recording techniques in future cadaveric and clinical studies. SIGNIFICANCE: This was the first study to evaluate progressive cartilage damage using AEs in a cadaver specimen. The findings of this study encourage further investigation of joint AE monitoring techniques.

Design and fabrication of a Wilkinson power divider with harmonic suppression for LTE and GSM applications.

Conventional Wilkinson power dividers (WPDs) can provide acceptable performance close to the nominal center frequency. However, these WPDs can also exhibit poor out-of-band performance while requiring a large footprint. In order to improve on the current state of the art, a modified microstrip WPD is proposed that exhibits a substantially improved stopband and high isolation. A lowpass filter (LPF) structure is utilized in both branches of the power divider to provide harmonic suppression. According to the obtained results, the input return loss (|S11|), output return loss (|S22|), output insertion loss (|S21|) and isolation (|S32|) are better than 34.2 dB, 26.2 dB, 3.52 dB and 31.2 dB, respectively. The proposed modified WPD has a wide 20 dB stopband (from 2.54 GHz to 13.48 GHz) and filters the second to seventh harmonics with attenuation levels of greater than 20 dB. The proposed WPD has a small size of 33.8 mm × 27 mm (0.42 λg × 0.33 λg), where λg is the guided wavelength at the operating frequency of 1.8 GHz. The WPD has been fabricated and tested and shows good agreement between simulated and measured results and the proposed design has desirable characteristics for LTE and GSM applications.

Test-Retest Reliability of Acoustic Emission Sensing of the Knee during Physical Tasks.

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises.

Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics.

Repetitive movements that involve a significant shift of the body's center of mass can lead to shoulder and elbow fatigue, which are linked to injury and musculoskeletal disorders if not addressed in time. Research has been conducted on the joint torque individuals can produce, a quantity that indicates the ability of the person to carry out such repetitive movements. Most of the studies surround gait analysis, rehabilitation, the assessment of athletic performance, and robotics. The aim of this study is to develop a model that estimates the maximum shoulder and elbow joint torque an individual can produce based on anthropometrics and demographics without taking a manual measurement with a force gauge (dynamometer). Nineteen subjects took part in the study which recorded maximum shoulder and elbow joint torques using a dynamometer. Sex, age, body composition parameters, and anthropometric data were recorded, and relevant parameters which significantly contributed to joint torque were identified using regression techniques. Of the parameters measured, body mass index and upper forearm volume predominantly contribute to maximum torque for shoulder and elbow joints; coefficient of determination values were between 0.6 and 0.7 for the independent variables and were significant for maximum shoulder joint torque (P<0.001) and maximum elbow joint torque (P<0.005) models. Two expressions illustrated the impact of the relevant independent variables on maximum shoulder joint torque and maximum elbow joint torque, using multiple linear regression. Coefficient of determination values for the models were between 0.6 and 0.7. The models developed enable joint torque estimation for individuals using measurements that are quick and easy to acquire, without the use of a dynamometer. This information is useful for those employing joint torque data in biomechanics in the areas of health, rehabilitation, ergonomics, occupational safety, and robotics. Clinical Relevance- The rapid estimation of arm joint torque without the direct force measurement can help occupational safety with the prevention of injury and musculoskeletal disorders in several working scenarios.

Highly Sensitive and Ultra-Responsive Humidity Sensors Based on Graphene Oxide Active Layers and High Surface Area Laser-Induced Graphene Electrodes.

Ultra-sensitive and responsive humidity sensors were fabricated by deposition of graphene oxide (GO) on laser-induced graphene (LIG) electrodes fabricated by a low-cost visible laser scribing tool. The effects of GO layer thickness and electrode geometry were investigated. Sensors comprising 0.33 mg/mL GO drop-deposited on spiral LIG electrodes exhibited high sensitivity up to 1800 pF/% RH at 22 °C, which is higher than previously reported LIG/GO sensors. The high performance was ascribed to the high density of the hydroxyl groups of GO, promoted by post-synthesis sonication treatment, resulting in high water physisorption rates. As a result, the sensors also displayed good stability and short response/recovery times across a wide tested range of 0-97% RH. The fabricated sensors were benchmarked against commercial humidity sensors and displayed comparable performance and stability. Finally, the sensors were integrated with a near-field communication tag to function as a wireless, battery-less humidity sensor platform for easy read-out of environmental humidity values using smartphones.

Smart Compression Therapy Devices for Treatment of Venous Leg Ulcers: A Review.

Venous leg ulcers can have significant social and economic impacts, and are generally treated by applying compression to the lower limb, which aids in promoting blood return to the heart. Compression therapies commonly involve the use of passive bandages that suffer from issues associated with incorrect application, and although automated solutions have begun to appear; these are often bulky and hinder mobility. Emerging microtechnologies and new materials enable the development of "smart" compression therapy devices, which are defined as systems that use miniaturized and lightweight actuators and electronics to control the applied pressure. This paper reviews the state of the art in smart compression therapy research. A total of seventeen different devices has been identified, categorized as using one of three actuation mechanisms: pneumatic compression, motor-driven mechanisms, and smart materials (including shape memory alloys, shape memory polymers, and electroactive polymers). The field is still in its relative infancy and further refinements are required to create mass manufacturable compression dressing systems that meet medical, ergonomic, and economic standards. The use of miniaturized actuators has immense potential for the development of smart compression dressings, which will ultimately lead to higher compliance, increased patient comfort, enhanced mobility, and better treatment outcomes.

A Comprehensive Survey on RF Energy Harvesting: Applications and Performance Determinants.

There has been an explosion in research focused on Internet of Things (IoT) devices in recent years, with a broad range of use cases in different domains ranging from industrial automation to business analytics. Being battery-powered, these small devices are expected to last for extended periods (i.e., in some instances up to tens of years) to ensure network longevity and data streams with the required temporal and spatial granularity. It becomes even more critical when IoT devices are installed within a harsh environment where battery replacement/charging is both costly and labour intensive. Recent developments in the energy harvesting paradigm have significantly contributed towards mitigating this critical energy issue by incorporating the renewable energy potentially available within any environment in which a sensor network is deployed. Radio Frequency (RF) energy harvesting is one of the promising approaches being investigated in the research community to address this challenge, conducted by harvesting energy from the incident radio waves from both ambient and dedicated radio sources. A limited number of studies are available covering the state of the art related to specific research topics in this space, but there is a gap in the consolidation of domain knowledge associated with the factors influencing the performance of RF power harvesting systems. Moreover, a number of topics and research challenges affecting the performance of RF harvesting systems are still unreported, which deserve special attention. To this end, this article starts by providing an overview of the different application domains of RF power harvesting outlining their performance requirements and summarizing the RF power harvesting techniques with their associated power densities. It then comprehensively surveys the available literature on the horizons that affect the performance of RF energy harvesting, taking into account the evaluation metrics, power propagation models, rectenna architectures, and MAC protocols for RF energy harvesting. Finally, it summarizes the available literature associated with RF powered networks and highlights the limitations, challenges, and future research directions by synthesizing the research efforts in the field of RF energy harvesting to progress research in this area.

Knowledge-driven feature engineering to detect multiple symptoms using ambulatory blood pressure monitoring data.

BACKGROUND: Hypertension is a major health concern across the globe and needs to be properly diagnosed to so it can be treated and to mitigate for this critical health condition. In this context, ambulatory blood pressure monitoring is essential to provide for a proper diagnosis of hypertension, which may not be possible otherwise due to the white coat effect or masked hypertension. In this paper, the objective is to develop a model which incorporates expert's knowledge in the feature engineering process so as to accurately predict multiple medical conditions. As a case study, we have considered multiple symptoms related to hypertension and used an ambulatory blood pressure monitoring method to continuously acquire hypertension relevant data from a patient. The goal is to train a model with a minimum set of the most effective knowledge-driven features which are useful to detect multiple symptoms simultaneously using multi-class classification techniques. METHOD: Artificial intelligence-based blood pressure monitoring techniques introduce a new dimension in the diagnosis of hypertension by enabling a continuous (24hours) analysis of systolic and diastolic blood pressure levels. In this work, we present a model that entails a knowledge-driven feature engineering method and implemented an ambulatory blood pressure monitoring system to diagnose multiple cardiac parameters and associated conditions simultaneously these include morning surge, circadian rhythm, and pulse pressure. The knowledge-driven features are extracted to improve the interpretability of the classification model and machine learning techniques (Random Forest, Naive Bayes, and KNN) were applied in a multi-label classification setup using RAkEL to classify multiple conditions simultaneously. RESULTS: The results obtained (F 1 = 0.918) show that the Random forest technique has performed well for multilabel classification using knowledge-driven features. Our technique has also reduced the complexity of the model by reducing the number of features required to train a machine learning model. CONCLUSION: Considering these results, we conclude that knowledge-driven feature engineering enhances the learning process by reducing the number of features given as input to the machine learning algorithm. The proposed feature engineering method considers expert's knowledge to develop better diagnosis models which are free from misleading data-driven noisy features in some situations. It is a white-box approach in which clinicians can under stand the importance of a feature while looking at its value.

Unsupervised IMU-based evaluation of at-home exercise programmes: a feasibility study.

BACKGROUND: The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may be used to determine at-home exercise performance and compliance to a physical therapy program. METHODS: A total of thirty healthy volunteers (mean age of 31 years) had their movements captured using wearable inertial measurement units (IMUs), after video recordings of five different exercises with varying levels of complexity were demonstrated to them. Participants were then given wearable sensors to enable a second unsupervised data capture at home. Movement performance between the participants' recordings was assessed with metrics of movement smoothness, intensity, consistency and control. RESULTS: In general, subjects executed all exercises similarly when recording at home and as compared with their performance in the lab. However, participants executed all movements faster compared to the physiotherapist's demonstrations, indicating the need of a wearable system with user feedback that will set the pace of movement. CONCLUSION: In light of the Covid-19 pandemic and the imperative transition towards remote consultation and tele-rehabilitation, this work aims to promote new tools and methods for the assessment of adherence to home-based physical therapy programmes. The studied IMU-derived features have shown adequate sensitivity to evaluate home-based programmes in an unsupervised manner. Cost-effective wearables, such as the one presented in this study, can support therapeutic exercises that ought to be performed with appropriate speed, intensity, smoothness and range of motion.

Flexible and Transparent Circularly Polarized Patch Antenna for Reliable Unobtrusive Wearable Wireless Communications.

This paper presents a circularly polarized flexible and transparent circular patch antenna suitable for body-worn wireless-communications. Circular polarization is highly beneficial in wearable wireless communications, where antennas, as a key component of the RF front-end, operate in dynamic environments, such as the human body. The demonstrated antenna is realized with highly flexible, robust and transparent conductive-fabric-polymer composite. The performance of the explored flexible-transparent antenna is also compared with its non-transparent counterpart manufactured with non-transparent conductive fabric. This comparison further demonstrates the suitability of the proposed materials for the target unobtrusive wearable applications. Detailed numerical and experimental investigations are explored in this paper to verify the proposed design. Moreover, the compatibility of the antenna in wearable applications is evaluated by testing the performance on a forearm phantom and calculating the specific absorption rate (SAR).

Wireless Communications for Smart Manufacturing and Industrial IoT: Existing Technologies, 5G and Beyond.

Smart manufacturing is a vision and major driver for change in today's industry. The goal of smart manufacturing is to optimize manufacturing processes through constantly monitoring, controlling, and adapting processes towards more efficient and personalised manufacturing. This requires and relies on technologies for connected machines incorporating a variety of computation, sensing, actuation, and machine to machine communications modalities. As such, understanding the change towards smart manufacturing requires knowledge of the enabling technologies, their applications in real world scenarios and the communication protocols and their performance to meet application requirements. Particularly, wireless communication is becoming an integral part of modern smart manufacturing and is expected to play an important role in achieving the goals of smart manufacturing. This paper presents an extensive review of wireless communication protocols currently applied in manufacturing environments and provides a comprehensive review of the associated use cases whilst defining their expected impact on the future of smart manufacturing. Based on the review, we point out a number of open challenges and directions for future research in wireless communication technologies for smart manufacturing.

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults.

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the "Healthy Ageing Initiative" study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.