The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Quantitative MRI for brain lesion diagnosis in dogs and cats: A comprehensive overview

MRI is widely used for the detection and characterization of brain lesions. There is a growing interest in the potential benefits of quantitative MRI (qMRI) in veterinary brain lesion diagnosis. Yet, the use of data processing tools in the veterinary field is not as democratized as for the diagnosis of human brain pathologies. Several reviews have addressed the characterization of brain lesions in cats and dogs. None of them is specifically focused on quantitative MRI data processing techniques for the diagnosis of brain lesions in the veterinary field. This paper aims to provide an overview of the evolution of qMRI on cats and dogs both in the clinical and preclinical fields. We analyze the achievements in the field as well as the remaining challenges in the diffusion of data processing tools for veterinary brain lesions characterization.

Gait classification of knee osteoarthritis patients using shoe-embedded internal measurement units sensor

BACKGROUND

Knee osteoarthritis negatively affects the gait of patients, especially that of elderly people. However, the assessment of wearable sensors in knee osteoarthritis patients has been under-researched. During clinical assessments, patients may change their gait patterns under the placebo effect, whereas wearable sensors can be used in any environment.

METHODS

Sixty patients with knee osteoarthritis and 20 control subjects were included in the study. Wearing shoes with an IMU sensor embedded in the insoles, the participants were required to walk along a walkway. The sensor data were collected during the gait. To discriminate between healthy and knee osteoarthritis patients and to classify different subgroups of knee osteoarthritis patients (patients scheduled for surgery vs. patients not scheduled for surgery; bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis), we used a machine learning approach called the support vector machine. A total of 88 features were extracted and used for classification.

FINDINGS

The patients vs. healthy participants were classified with 71% accuracy, 85% sensitivity, and 56% specificity. The "patients scheduled for surgery" vs. "patients not scheduled for surgery" were classified with 83% accuracy, 83% sensitivity, and 81% specificity. The bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis was classified with 81% accuracy, 75% sensitivity, and 79% specificity.

INTERPRETATION

Gait analysis using wearable sensors and machine learning can discriminate between healthy and knee osteoarthritis patients and classify different subgroups with reasonable accuracy, sensitivity, and specificity. The proposed approach requires no complex gait factors and is not limited to controlled laboratory settings.

Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review

The rapid development of biosensing technologies together with the advent of deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, and health-specific technologies have the potential to facilitate remote and accessible diagnosis, monitoring, and adaptive therapy in a naturalistic environment. This systematic review focuses on the impact of combining multiple biosensing techniques with deep learning algorithms and the application of these models to healthcare. We explore the key areas that researchers and engineers must consider when developing a deep learning model for biosensing: the data modality, the model architecture, and the real-world use case for the model. We also discuss key ongoing challenges and potential future directions for research in this field. We aim to provide useful insights for researchers who seek to use intelligent biosensing to advance precision healthcare.

Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review

BACKGROUND

Children and adolescents with neuromotor disorders need regular physical activity to maintain optimal health and functional independence throughout their development. To this end, reliable measures of physical activity are integral to both assessing habitual physical activity and testing the efficacy of the many interventions designed to increase physical activity in these children. Wearable accelerometers have been used for children with neuromotor disorders for decades; however, studies most often use disorder-specific cut points to categorize physical activity intensity, which lack generalizability to a free-living environment. No reviews of accelerometer data processing methods have discussed the novel use of machine learning techniques for monitoring physical activity in children with neuromotor disorders.

METHODS

In this narrative review, we discuss traditional measures of physical activity (including questionnaires and objective accelerometry measures), the limitations of standard analysis for accelerometry in this unique population, and the potential benefits of applying machine learning approaches. We also provide recommendations for using machine learning approaches to monitor physical activity.

CONCLUSIONS

While wearable accelerometers provided a much-needed method to quantify physical activity, standard cut point analyses have limitations in children with neuromotor disorders. Machine learning models are a more robust method of analyzing accelerometer data in pediatric neuromotor disorders and using these methods over disorder-specific cut points is likely to improve accuracy of classifying both type and intensity of physical activity. Notably, there remains a critical need for further development of classifiers for children with more severe motor impairments, preschool aged children, and children in hospital settings.

Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning

Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura-Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP.

The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review

The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.

Remote Patient Monitoring Following Total Joint Arthroplasty

This review article presents the current state of remote patient monitoring (RPM) in total joint arthroplasty. RPM refers to the use of telecommunication with wearable and implantable technology to assess and treat patients. Several forms of RPM are discussed including telemedicine, patient engagement platforms, wearable devices, and implantable devices. The benefits to patients and physicians are discussed in the context of postoperative monitoring. Insurance coverage and reimbursement of these technologies are reviewed.

Involvement of Human Volunteers in the Development and Evaluation of Wearable Devices Designed to Improve Medication Adherence: A Scoping Review

Wearable devices designed to improve medication adherence can emit audible and vibrating alerts or send text messages to users. However, there is little information on the validation of these technologies. The aim of this scoping review was to investigate the involvement of human volunteers in the development and evaluation of wearable devices. A literature search was conducted using six databases (MEDLINE, Embase, Scopus, CINAHL, PsycInfo, and Web of Science) up to March 2020. A total of 7087 records were identified, and nine studies were included. The wearable technologies most investigated were smartwatches (n = 3), patches (n = 3), wristbands (n = 2), and neckwear (n = 1). The studies involving human volunteers were categorized into idea validation (n = 4); prototype validation (n = 5); and product validation (n = 1). One of them involved human volunteers in idea and prototype validation. A total of 782 participants, ranging from 6 to 252, were included. Only five articles reported prior approval by a research ethics committee. Most studies revealed fragile methodological designs, a lack of a control group, a small number of volunteers, and a short follow-up time. Product validation is essential for regulatory approval and encompasses the assessment of the effectiveness, safety, and performance of a wearable device. Studies with greater methodological rigor and the involvement of human volunteers can contribute to the improvement of the process before making them available on the market.

Patient Perceptions of Wearable and Smartphone Technologies for Remote Outcome Monitoring in Patients Who Have Hip Osteoarthritis or Arthroplasties

BACKGROUND

Although there is interest in wearables and smartphone technologies for remote outcome monitoring, little is known regarding the willingness of hip osteoarthritis (OA) and/or total hip arthroplasty (THA) patients to authorize and adhere to such treatment.

METHODS

We developed an Institutional Review Board-approved questionnaire to evaluate patient perceptions of remote monitoring technologies in a high-volume orthopedic center. Forty-seven THA patients (60% female; mean age: 66 years) and 50 nonoperative OA hip patients (52% female; mean age: 63 years) participated. Patient perceptions were compared using Pearson's chi-squared analyses.

RESULTS

THA patients were similarly interested in the use of smartphone apps (91% vs 94%, P = .695) in comparison to nonoperative hip OA patients. THA patients were more receptive to using wearable sensors (94% vs 44%, P < .001) relative to their nonoperative counterparts. THA patients also expressed stronger interest in learning to use custom wearables (87% vs 32%, P < .001) vs nonoperative patients. Likewise, the majority of THA patients were willing to use Global Positioning System technology (74% vs 26%, P < .001). THA patients also expressed willingness to have their body movement (89%), balance (89%), sleep (87%), and cardiac output (91%) tracked using remote technology.

CONCLUSION

Overall, we found that THA patients were highly receptive to using wearable technology in their treatments. Nonoperative OA hip patients were generally unreceptive to using smart technologies, with the exception of smartphone applications. This information may be useful as utilization of these technologies for patient care continues to evolve.

Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools

The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population.

Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design

Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

Diagnostic value of a vision-based intelligent gait analyzer in screening for gait abnormalities

BACKGROUND

Early detection of gait abnormalities is critical for preventing severe injuries in future falls. The timed up and go (TUG) test is a commonly used clinical gait screening test; however, the interpretation of its results is limited to the TUG total time.

RESEARCH QUESTION

What is diagnostic accuracy of the low-cost, markerless, automated gait analyzer, with the aid of vision-based artificial intelligence technology, which extract gait spatiotemporal features and screen for abnormal walking patterns through video recordings of the TUG test?

METHODS

Our dataset contained retrospective data from outpatients from the Department of Neurology or Rehabilitation of two tertiary hospitals in Shanghai. A panel of three expert neurologists specialized in movement disorders reviewed the gait performance in each TUG video, and labeled them separately, with the most commonly assigned label being used as the reference standard. The gait analyzer performed the AlphaPose algorithm to track the human joint position and calculated the spatiotemporal parameters by filtering and double-threshold signal detection. Gait spatiotemporal features and expert labels were input into machine learning models, and the accuracy of each model was tested with leave-one-out cross-validation (LOOCV).

RESULTS

A total of 284 participants were recruited. Among these, 100 were labeled as having abnormal gait performance by experts. The Naive Bayes classifier achieved the best performance with a full-data accuracy of 90.14% and a LOOCV accuracy of 89.08% for screening abnormal gait performance.

SIGNIFICANCE

This study is the first to investigate the accuracy of a vision-based intelligent gait analyzer for screening abnormal clinical gait performance. By virtue of a pose estimation algorithm and machine learning models, our intelligent gait analyzer can detect abnormal walking patterns approximate to judgements made by experienced neurologists, which is expected to be a supplementary gait assessment protocol for basic-level doctors in the future.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.