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

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4808
Author(s):  
Théo Jourdan ◽  
Noëlie Debs ◽  
Carole Frindel
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Patient Adherence ◽  
Wearable Sensors ◽  
Home Monitoring ◽  
Assessment Tools ◽  
Learning Approaches ◽  
Ground Truth Data ◽  
Validation Methods ◽  
Gait Monitoring

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

scholarly journals Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 448
Author(s):  
Alessandro Tonacci ◽  
Alessandro Dellabate ◽  
Andrea Dieni ◽  
Lorenzo Bachi ◽  
Francesco Sansone ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress Reduction ◽  
Wearable Sensors ◽  
Well Being ◽  
Significant Heterogeneity ◽  
Learning Approaches ◽  
Significant Difference ◽  
Group A ◽  
Group B ◽  
Audio Video

Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.

scholarly journals Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 88
Author(s):  
Charles Carslake ◽  
Jorge A. Vázquez-Diosdado ◽  
Jasmeet Kaler
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Wearable Sensors ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Dairy Calves ◽  
Learning Approaches ◽  
True Prevalence ◽  
Play Behaviour ◽  
Highly Correlated

Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In calves, multiple behaviours such as locomotor play, self-grooming, feeding and activity whilst lying are likely to be informative. However, these behaviours can occur rarely in the real world, which means simply counting behaviours based on the prediction of a classifier can lead to overestimation. Here, we equipped thirteen pre-weaned dairy calves with collar-mounted sensors and monitored their behaviour with video cameras. Behavioural observations were recorded and merged with sensor signals. Features were calculated for 1–10-s windows and an AdaBoost ensemble learning algorithm implemented to classify behaviours. Finally, we developed an adjusted count quantification algorithm to predict the prevalence of locomotor play behaviour on a test dataset with low true prevalence (0.27%). Our algorithm identified locomotor play (99.73% accuracy), self-grooming (98.18% accuracy), ruminating (94.47% accuracy), non-nutritive suckling (94.96% accuracy), nutritive suckling (96.44% accuracy), active lying (90.38% accuracy) and non-active lying (90.38% accuracy). Our results detail recommended sampling frequencies, feature selection and window size. The quantification estimates of locomotor play behaviour were highly correlated with the true prevalence (0.97; p < 0.001) with a total overestimation of 18.97%. This study is the first to implement machine learning approaches for multi-class behaviour identification as well as behaviour quantification in calves. This has potential to contribute towards new insights to evaluate the health and welfare in calves by use of wearable sensors.

scholarly journals Using Machine Learning to Advance Personality Assessment and Theory

2018 ◽  
Vol 23 (2) ◽  
pp. 190-203 ◽  
Author(s):  
Wiebke Bleidorn ◽  
Christopher James Hopwood
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Personality Assessment ◽  
Construct Validation ◽  
Assessment Tools ◽  
Learning Approaches ◽  
Psychological Science ◽  
Validation Framework ◽  
Learning Research ◽  
Applications Of Machine Learning

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

scholarly journals Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

2020 ◽  
Author(s):  
Helen Spiers ◽  
Harry Songhurst ◽  
Luke Nightingale ◽  
Joost de Folter ◽  
Roger Hutchings ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Machine Learning ◽  
Nuclear Envelope ◽  
Citizen Science ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Quality ◽  
Ground Truth Data

AbstractAdvancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

scholarly journals Using Machine Learning to Advance Personality Assessment and Theory

2018 ◽  
Author(s):  
Wiebke Bleidorn ◽  
Christopher James Hopwood
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Personality Assessment ◽  
Construct Validation ◽  
Assessment Tools ◽  
Learning Approaches ◽  
Psychological Science ◽  
Validation Framework ◽  
Learning Research ◽  
Applications Of Machine Learning

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have been focused on the associations between social media and other digital records with established personality measures. The goal of this paper is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation and provide recommendations for how to use machine learning to advance our understanding of personality.

scholarly journals Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

2022 ◽  
Author(s):  
Aishwarya Balakrishnan ◽  
◽  
Jeevan Medikonda ◽  
Pramod Kesavan Namboothiri ◽  
Manikandan Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Wearable Sensors ◽  
Disease Assessment ◽  
Learning Approaches

scholarly journals Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

2021 ◽  
pp. 167-175
Author(s):  
Megan K. O’Brien ◽  
Olivia K. Botonis ◽  
Elissa Larkin ◽  
Julia Carpenter ◽  
Bonnie Martin-Harris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Measurement Techniques ◽  
Sensor Data ◽  
Patient Specific ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Wearable Sensor ◽  
Complex Signals ◽  
Promising Alternative

<b><i>Introduction:</i></b> Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning. <b><i>Methods:</i></b> Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls). <b><i>Results:</i></b> These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (<i>p</i> &#x3c; 0.037). <b><i>Discussion:</i></b> Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Sign in / Sign up

Export Citation Format

Share Document