Correlation between proprioception, functionality, patient-reported knee condition and joint acoustic emissions.

Non-invasive assessment of joint status using acoustic emissions (AE) is a growing research area that has the potential to translate into clinical practice. The purpose of this study is to investigate the correlation of the knee's AE with measures of proprioception, self-assessment, and performance, as it can be hypothesised that, AE parameters will correlate with joint function metrics due to AE being recorded during interaction of the articular surfaces. Threshold to detect passive motion (TTDPM), Knee Osteoarthritis Outcome Scores (KOOS) and 5 times sit-to-stand test (5STS) were collected from 51 participant.

Wearable-Enabled Algorithms for the Estimation of Parkinson's Symptoms Evaluated in a Continuous Home Monitoring Setting Using Inertial Sensors.

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson's disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of this study is to demonstrate the feasibility of detecting symptom episodes in a free-living scenario, providing a higher level of interpretability to aid AI-powered decision-making. Machine learning models trained on wearable sensor data from scripted activities performed by participants in the lab and clinician ratings of the video recordings of these tasks identified tremor, bradykinesia, and dyskinesia in the supervised lab environment with a balanced accuracy of 83%, 75%, and 81%, respectively, when compared to the clinician ratings.

Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings.

Purpose: The aim of this study is to explore the feasibility of using machine learning approaches to objectively differentiate the mobilization patterns, measured via accelerometer sensors, of patients pre- and post-intervention.

Methods: The intervention tested the implementation of a Frailty Care Bundle to improve mobilization, nutrition and cognition in older orthopedic patients. The study recruited 120 participants, a sub-group analysis was undertaken on 113 patients with accelerometer data (57 pre-intervention and 56 post-intervention), the median age was 78 years and the majority were female.

Development of a Personalized Multiclass Classification Model to Detect Blood Pressure Variations Associated with Physical or Cognitive Workload.

Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure.

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.

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.

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.

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 and applications using a broad range of materials, is proving to be a promising approach.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Motion Artifact Resistant Mounting of Acoustic Emission Sensors for Knee Joint Monitoring.

Among the many diverse methods of recording biological signals, sound and acoustic emission monitoring are becoming popular for data acquisition; however, these sensors tend to be very susceptible to motion artefacts and noise. In the case of joint monitoring, this issue is even more significant, considering that joint sounds are recorded during limb movements to establish joint health and performance. This paper investigates different sensor attachment methods for acoustic emission monitoring of the knee, which could lead to reduced motion and skin movement artefacts and improve the quality of sensory data sets.

Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults.

Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality.