Wearable systems for shoulder kinematics assessment: a systematic review

Abstract Background Wearable sensors are acquiring more and more influence in diagnostic and rehabilitation field to assess motor abilities of people with neurological or musculoskeletal impairments. The aim of this systematic literature review is to analyze the wearable systems for monitoring shoulder kinematics and their applicability in clinical settings and rehabilitation. Methods A comprehensive search of PubMed, Medline, Google Scholar and IEEE Xplore was performed and results were included up to July 2019. All studies concerning wearable sensors to assess shoulder kinematics were retrieved. Results Seventy-three studies were included because they have fulfilled the inclusion criteria. The results showed that magneto and/or inertial sensors are the most used. Wearable sensors measuring upper limb and/or shoulder kinematics have been proposed to be applied in patients with different pathological conditions such as stroke, multiple sclerosis, osteoarthritis, rotator cuff tear. Sensors placement and method of attachment were broadly heterogeneous among the examined studies. Conclusions Wearable systems are a promising solution to provide quantitative and meaningful clinical information about progress in a rehabilitation pathway and to extrapolate meaningful parameters in the diagnosis of shoulder pathologies. There is a strong need for development of this novel technologies which undeniably serves in shoulder evaluation and therapy.

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Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Sensors ◽

10.3390/s19194075 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4075 ◽

Cited By ~ 25

Author(s):

Marco Ghislieri ◽

Laura Gastaldi ◽

Stefano Pastorelli ◽

Shigeru Tadano ◽

Valentina Agostini

Keyword(s):

Inertial Sensors ◽

Postural Sway ◽

Wearable Sensors ◽

Critical Discussion ◽

Standing Balance ◽

Wearable Systems ◽

The Subject ◽

Wearable Inertial Sensors ◽

Selection Of

Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen’s kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., “active aging”, biofeedback-based rehabilitation for fall prevention, and the management of Parkinson’s disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

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Recent Advances in Wearable Devices for Non-Invasive Sensing

Applied Sciences ◽

10.3390/app11031235 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1235

Author(s):

Su Min Yun ◽

Moohyun Kim ◽

Yong Won Kwon ◽

Hyobeom Kim ◽

Mi Jung Kim ◽

...

Keyword(s):

Health Monitoring ◽

Data Transmission ◽

Wearable Sensors ◽

Wearable Devices ◽

Wireless Power ◽

Non Invasive ◽

Sensing Systems ◽

Recent Advances ◽

Wearable Systems ◽

Precise Diagnosis

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.

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Multi-omics approach to precision medicine for immune-mediated diseases

Inflammation and Regeneration ◽

10.1186/s41232-021-00173-8 ◽

2021 ◽

Vol 41 (1) ◽

Author(s):

Mineto Ota ◽

Keishi Fujio

Keyword(s):

Treatment Response ◽

High Throughput Sequencing ◽

Disease Risk ◽

Clinical Information ◽

Clinical Settings ◽

Social Significance ◽

Sequencing Technologies ◽

Immune Mediated ◽

Recent Innovation ◽

Future Direction

AbstractRecent innovation in high-throughput sequencing technologies has drastically empowered the scientific research. Consequently, now, it is possible to capture comprehensive profiles of samples at multiple levels including genome, epigenome, and transcriptome at a time. Applying these kinds of rich information to clinical settings is of great social significance. For some traits such as cardiovascular diseases, attempts to apply omics datasets in clinical practice for the prediction of the disease risk have already shown promising results, although still under way for immune-mediated diseases. Multiple studies have tried to predict treatment response in immune-mediated diseases using genomic, transcriptomic, or clinical information, showing various possible indicators. For better prediction of treatment response or disease outcome in immune-mediated diseases, combining multi-layer information together may increase the power. In addition, in order to efficiently pick up meaningful information from the massive data, high-quality annotation of genomic functions is also crucial. In this review, we discuss the achievement so far and the future direction of multi-omics approach to immune-mediated diseases.

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Wearable Sensors Integrated with Virtual Reality: A Self-Guided Healthcare System Measuring Shoulder Joint Mobility for Frozen Shoulder

Journal of Healthcare Engineering ◽

10.1155/2019/7681237 ◽

2019 ◽

Vol 2019 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Jianjun Cui ◽

Shih-Ching Yeh ◽

Si-Huei Lee

Keyword(s):

Virtual Reality ◽

Shoulder Joint ◽

Inertial Sensors ◽

Wearable Sensors ◽

Frozen Shoulder ◽

Joint Mobility ◽

Joint Movement ◽

Rehabilitation Process ◽

Shoulder Condition ◽

At Home

Frozen shoulder is a common clinical shoulder condition. Measuring the degree of shoulder joint movement is crucial to the rehabilitation process. Such measurements can be used to evaluate the severity of patients’ condition, establish rehabilitation goals and appropriate activity difficulty levels, and understand the effects of rehabilitation. Currently, measurements of the shoulder joint movement degree are typically conducted by therapists using a protractor. However, along with the growth of telerehabilitation, measuring the shoulder joint mobility on patients’ own at home will be needed. In this study, wireless inertial sensors were combined with the virtual reality interactive technology to provide an innovative shoulder joint mobility self-measurement system that can enable patients to measure their performance of four shoulder joint movements on their own at home. Pilot clinical trials were conducted with 25 patients to confirm the feasibility of the system. In addition, the results of correlation and differential analyses compared with the results of traditional measurement methods exhibited a high correlation, verifying the accuracy of the proposed system. Moreover, according to interviews with patients, they are confident in their ability to measure shoulder joint mobility themselves.

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Combined evaluation of genotype and phenotype of thiopurine S-methyltransferase (TPMT) in the clinical management of patients in chronic therapy with azathioprine

Drug Metabolism and Personalized Therapy ◽

10.1515/dmpt-2018-0037 ◽

2019 ◽

Vol 34 (1) ◽

Author(s):

Francesco Rucci ◽

Maria Sole Cigoli ◽

Valeria Marini ◽

Carmen Fucile ◽

Francesca Mattioli ◽

...

Keyword(s):

Enzyme Activity ◽

Adverse Events ◽

High Performance ◽

Clinical Information ◽

Variant Allele ◽

Clinical Settings ◽

Wild Type ◽

Chronic Therapy ◽

Significant Difference ◽

Combined Evaluation

Abstract Background The thiopurine S-methyltransferase (TPMT)/azathioprine (AZA) gene-drug pair is one of the most well-known pharmacogenetic markers. Despite this, few studies investigated the implementation of TPMT testing and the combined evaluation of genotype and phenotype in multidisciplinary clinical settings where patients are undergoing chronic therapy with AZA. Methods A total of 356 AZA-treated patients for chronic autoimmune diseases were enrolled. DNA was isolated from whole blood and the samples were analyzed for the c.460G>A and c.719A>G variants by the restriction fragment length polymorphism (RFLP) technique and sequenced for the c.238G>C variant. The TPMT enzyme activity was determined in erythrocytes by a high-performance liquid chromatography (HPLC) assay. Results All the patients enrolled were genotyped while the TPMT enzyme activity was assessed in 41 patients. Clinical information was available on 181 patients. We found no significant difference in the odds of having adverse drug reactions (ADRs) in wild-type patients and variant allele carriers, but the latter had an extra risk of experiencing hematologically adverse events. The enzyme activity was significantly associated to genotype. Conclusions TPMT variant allele carriers have an extra risk of experiencing hematologically adverse events compared to wild-type patients. Interestingly, only two out of 30 (6.6%) patients had discordant results between genotype, phenotype and onset of ADRs.

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Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽

10.3390/s18124132 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4132 ◽

Cited By ~ 10

Author(s):

Ku Ku Abd. Rahim ◽

I. Elamvazuthi ◽

Lila Izhar ◽

Genci Capi

Keyword(s):

Activity Recognition ◽

Inertial Sensors ◽

Wearable Sensors ◽

Ensemble Methods ◽

Daily Activities ◽

Support Vector ◽

Random Subspace ◽

Ensemble Classifiers ◽

Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

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Scapular Dyskinesis: From Basic Science to Ultimate Treatment

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17082974 ◽

2020 ◽

Vol 17 (8) ◽

pp. 2974 ◽

Cited By ~ 1

Author(s):

Longo Umile Giuseppe ◽

Risi Ambrogioni Laura ◽

Alessandra Berton ◽

Vincenzo Candela ◽

Carlo Massaroni ◽

...

Keyword(s):

Scapular Dyskinesis ◽

Clinical Tests ◽

High Quality ◽

Scapular Kinematics ◽

Multiple Factors ◽

Comprehensive Search ◽

Shoulder Kinematics ◽

And Function ◽

Shoulder Pathologies ◽

Scapular Motion

Background: This study intends to summarize the causes, clinical examination, and treatments of scapular dyskinesis (SD) and to briefly investigate whether alteration can be managed by a precision rehabilitation protocol planned on the basis of features derived from clinical tests. Methods: We performed a comprehensive search of PubMed, Cochrane, CINAHL and EMBASE databases using various combinations of the keywords “Rotator cuff”, “Scapula”, “Scapular Dyskinesis”, “Shoulder”, “Biomechanics” and “Arthroscopy”. Results: SD incidence is growing in patients with shoulder pathologies, even if it is not a specific injury or directly related to a particular injury. SD can be caused by multiple factors or can be the trigger of shoulder-degenerative pathologies. In both cases, SD results in a protracted scapula with the arm at rest or in motion. Conclusions: A clinical evaluation of altered shoulder kinematics is still complicated. Limitations in observing scapular motion are mainly related to the anatomical position and function of the scapula itself and the absence of a tool for quantitative SD clinical assessment. High-quality clinical trials are needed to establish whether there is a possible correlation between SD patterns and the specific findings of shoulder pathologies with altered scapular kinematics.

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Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Sensors ◽

10.3390/s20030622 ◽

2020 ◽

Vol 20 (3) ◽

pp. 622

Author(s):

Francisco Javier González-Cañete ◽

Eduardo Casilari

Keyword(s):

Wireless Sensors ◽

Inertial Sensors ◽

Cost Effective ◽

Fall Detection ◽

Monitoring Point ◽

Research Attention ◽

Battery Lifetime ◽

Detection Systems ◽

Wearable Systems ◽

Multiple Sensing

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

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An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors

Sensors ◽

10.3390/s19112644 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2644

Author(s):

Vladislava Bobić ◽

Milica Djurić-Jovičić ◽

Nataša Dragašević ◽

Mirjana B. Popović ◽

Vladimir S. Kostić ◽

...

Keyword(s):

Clinical Practice ◽

Decision Support ◽

Expert System ◽

Inertial Sensors ◽

Wearable Sensors ◽

Objective Evaluation ◽

Finger Tapping ◽

Symptom Evaluation ◽

Wearable Inertial Sensors ◽

Processing Techniques

Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.

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Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Sensors ◽

10.3390/s20010082 ◽

2019 ◽

Vol 20 (1) ◽

pp. 82 ◽

Cited By ~ 1

Author(s):

Udeni Jayasinghe ◽

William S. Harwin ◽

Faustina Hwang

Keyword(s):

Inertial Sensors ◽

Inertial Sensor ◽

Wearable Sensors ◽

Correlation Coefficients ◽

Movement Analysis ◽

Multiple Sensors ◽

Sensor Orientation ◽

Multiple Devices ◽

Good Potential ◽

Sensor Errors

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

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