The world’s population is aging: the expansion of the older adult population with multiple physical and health issues is now a huge socio-economic concern worldwide. Among these issues, the loss of mobility among older adults due to musculoskeletal disorders is especially serious as it has severe social, mental and physical consequences. Human body joint monitoring and early diagnosis of these disorders will be a strong and effective solution to this problem. A smart joint monitoring system can identify and record important musculoskeletal-related parameters. Such devices can be utilized for continuous monitoring of joint movements during the normal daily activities of older adults and the healing process of joints (hips, knees or ankles) during the post-surgery period. A viable monitoring system can be developed by combining miniaturized, durable, low-cost and compact sensors with the advanced communication technologies and data processing techniques. In this study, we have presented and compared different joint monitoring methods and sensing technologies recently reported. A discussion on sensors’ data processing, interpretation, and analysis techniques is also presented. Finally, current research focus, as well as future prospects and development challenges in joint monitoring systems are discussed.

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.

Maintaining good mobility with ease and freedom of movement is important for an individual’s health and active aging. The knee joint, being the primary bearer of the body weight, plays a vital role in mobility. Continuous monitoring of the knee joint can potentially provide important information related to knee health and mobility which can be used for health assessment, early diagnoses of mobility-related problems, and monitoring recovery from injury or surgery. Therefore, we developed a simple, low-cost multi-sensor-based smart wearable device to monitor and assess the knee joint and mobility. The system is composed of miniaturized sensors (motion, temperature, pressure and galvanic skin response) to measure acceleration, angular velocity, skin temperature, muscle pressure and sweat rate of the knee joint during different activities. A database is constructed from 70 healthy adults aged 18-86 years that contains sensor data measured using the proposed knee joint monitoring system. To extract key knee and gait features from the datasets, we employed computationally efficient methods such as complementary filter and wavelet packet decomposition. The variations in the characteristics of the obtained parameters were analyzed in terms of gender and age groups. This simple, easy–to–use, cost-effective, non-invasive and unobtrusive knee monitoring system can be used for real-time monitoring, evaluation and early diagnoses of joint disorders, fall detection, mobility monitoring and rehabilitation.

This meta-analysis and systematic review, conducted in accordance with PRISMA guidelines, explores the efficacy of cuff-less blood pressure (BP) monitoring methods, particularly focusing on photoplethysmogram-based technologies. This comprehensive analysis carefully searched prominent databases such as MEDLINE, PubMed, AMED, Embase, and IEEE-Xplore, encompassing 25 studies with a collective participant pool of 21,142 individuals. The study primarily investigates the accuracy and practicality of continuous BP estimation devices and algorithms, aiming to assess their suitability for daily or long-term, as well as their applicability and usability across a broad population. The mean disparities were 4.14 mmHg for systolic blood pressure (SBP) and 2.79 mmHg for diastolic blood pressure (DBP), highlighting a close congruence with established measurement techniques. An in-depth analysis into specific methodologies reveals that Pulse Waveform Analysis (PWA) demonstrates a more favorable performance compared to Pulse Wave Velocity (PWV) for both SBP and DBP, although these differences are not statistically significant. The findings indicate a promising future for wearable devices in short-term BP monitoring scenarios. Both PWA and PWV methods in wearable formats have shown considerable potential as effective tools for BP assessment. However, the study underscores the need for further research, particularly targeting hypertensive populations, to validate the long-term effectiveness and reliability of these wearables. Finally, this investigation is crucial for establishing the role of wearables in ongoing, reliable BP monitoring, especially when considered in conjunction with other health monitoring technologies.

Mobility is crucial for healthy aging. Any disruption to mobility can affect mental, physical and social health, and socio-economic independence. Therefore, studies in gait and lower-joint functionality with respect to different demographic features will play a vital role in maintaining good mobility. In this study, we analyzed a gait database from 70 healthy subjects (18–86 years) constructed using our custom-built multi-sensor-based wearable tele-health monitoring system. The purpose was to extract and use the most informative features for classifying knee joint and gait characteristics of the subjects with respect to their age, body mass index – BMI, and sex. Four supervised machine learning algorithms: partial least square-discriminant analysis (PLS-DA), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were used to classify the subjects. The features that significantly contributed to all classifications are knee angle, quadriceps muscle pressure adjacent to the knee joint, rotational energy (mediolateral and vertical), acceleration energy (mediolateral), cross-sample entropy (anteroposterior-mediolateral), knee angle variability, symmetry of swing and stance phase, and walk ratio. Classification accuracies of all four methods were ~89%, 83%, 81%, 86% for age, 90%, 80%, 83%, 86% for BMI, and 97%, 97%, 96%, 97% for sex, respectively. PLS-DA had the best classification performance for all three categories which makes it preferable for these kinds of analyses. Thus, our knee and gait monitoring system coupled with an efficient machine learning tool can be exploited for real-time evaluation and early diagnoses of mobility disabilities, health assessment, and monitoring the need for interventions.

Human gait analysis aims to assess gait mechanics and to identify the deviations from “normal” gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 – 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (∼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.