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

Sensors ◽  
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.

scholarly journals Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1471 ◽  
Author(s):  
Tobias Steinmetzer ◽  
Michele Maasch ◽  
Ingrid Bönninger ◽  
Carlos M. Travieso
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Wavelet Transformation ◽  
Inertial Sensors ◽  
Arm Movement ◽  
Objective Evaluation ◽  
Timed Up And Go ◽  
Arm Swing ◽  
Age Related ◽  
Wearable Inertial Sensors

Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.

scholarly journals Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4075 ◽  
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.

scholarly journals Empowering Patients in Self-Management of Parkinson's Disease through Cooperative ICT Systems

2016 ◽  
pp. 251-277 ◽  
Author(s):  
Erika Rovini ◽  
Dario Esposito ◽  
Carlo Maremmani ◽  
Paolo Bongioanni ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Objective Evaluation ◽  
Integrated Devices ◽  
Quantitative Metrics ◽  
Medical Effectiveness ◽  
Health And Disease ◽  
Wearable Inertial Sensors ◽  
Cloud Technologies

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.

scholarly journals Empowering Patients in Self-Management of Parkinson's Disease Through Cooperative ICT Systems

2018 ◽  
pp. 637-663
Author(s):  
Erika Rovini ◽  
Dario Esposito ◽  
Carlo Maremmani ◽  
Paolo Bongioanni ◽  
Filippo Cavallo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Objective Evaluation ◽  
Integrated Devices ◽  
Quantitative Metrics ◽  
Medical Effectiveness ◽  
Health And Disease ◽  
Wearable Inertial Sensors ◽  
Cloud Technologies

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies.

scholarly journals Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254813
Author(s):  
Eloise V. Briggs ◽  
Claudia Mazzà
Keyword(s):  
Event Detection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Force Plate ◽  
Field Conditions ◽  
Future Studies ◽  
Gait Classification ◽  
Automatic Methods ◽  
Wearable Inertial Sensors

Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.

scholarly journals Toward a Regulatory Qualification of Real-World Mobility Performance Biomarkers in Parkinson’s Patients Using Digital Mobility Outcomes

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5920
Author(s):  
Marco Viceconti ◽  
Sabina Hernandez Penna ◽  
Wilhelmus Dartee ◽  
Claudia Mazzà ◽  
Brian Caulfield ◽  
...  
Keyword(s):  
Real World ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Human Movement ◽  
European Medicines Agency ◽  
Drug Trials ◽  
Mobility Performance ◽  
Mobility Assessment ◽  
Drug Regulatory Agencies ◽  
Wearable Inertial Sensors

Wearable inertial sensors can be used to monitor mobility in real-world settings over extended periods. Although these technologies are widely used in human movement research, they have not yet been qualified by drug regulatory agencies for their use in regulatory drug trials. This is because the first generation of these sensors was unreliable when used on slow-walking subjects. However, intense research in this area is now offering a new generation of algorithms to quantify Digital Mobility Outcomes so accurate they may be considered as biomarkers in regulatory drug trials. This perspective paper summarises the work in the Mobilise-D consortium around the regulatory qualification of the use of wearable sensors to quantify real-world mobility performance in patients affected by Parkinson’s Disease. The paper describes the qualification strategy and both the technical and clinical validation plans, which have recently received highly supportive qualification advice from the European Medicines Agency. The scope is to provide detailed guidance for the preparation of similar qualification submissions to broaden the use of real-world mobility assessment in regulatory drug trials.

Badminton stroke identification using wireless inertial sensor and neural network

2021 ◽  
pp. 175433712110483
Author(s):  
Jian Hui Ooi ◽  
Darwin Gouwanda
Keyword(s):  
Neural Network ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Objective Evaluation ◽  
Training Algorithm ◽  
Segmentation Method ◽  
Competitive Environments ◽  
Risk Of Injury ◽  
Timely Feedback ◽  
Wearable Inertial Sensors

Objective evaluation is essential in sports to monitor athlete performance, provide relevant and timely feedback, and minimize the risk of injury. Activity recognition is the first step in sport skill and technique performance analysis. This study investigated the use of wearable inertial sensors and a neural network (NN) to identify badminton strokes. The study also explored the effect of different NN configurations and a different number of sensors on the classification. Sensors were placed at the dominant wrist, left ankle, and right ankle. Six different strokes, ranging from soft hitting net shots to smashes, were performed with a total of 3300 repetitions from six well-trained badminton players. An automated window segmentation method was developed to identify the stroke instances. A scaled conjugate gradient training algorithm with two hidden layers and 55 neurons in each layer was found to be the best approach to classify badminton strokes with an accuracy of 97.69%. Even just wearing the inertial sensor on the wrist was sufficient, providing an accuracy of 95.09%. These results demonstrate the viability of using inertial sensors and NN to recognize badminton strokes, which can be applied in training and competitive environments.

scholarly journals Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2564 ◽  
Author(s):  
Andrea Ancillao ◽  
Salvatore Tedesco ◽  
John Barton ◽  
Brendan O’Flynn
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Reaction Force ◽  
Performance Testing ◽  
Indirect Measurement ◽  
Sport Performance ◽  
Ground Reaction Forces ◽  
Kinematic Data ◽  
Reaction Forces ◽  
Wearable Inertial Sensors

In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

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