scholarly journals Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers

10.2196/17289 ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. e17289
Author(s):  
Reza Haghighi Osgouei ◽  
David Soulsby ◽  
Fernando Bello
Keyword(s):  
Machine Learning ◽  
Exercise Program ◽  
Machine Learning Algorithms ◽  
Qualitative Assessment ◽  
Shoulder Abduction ◽  
General View ◽  
Skeleton Model ◽  
Movement Data ◽  
Hip Abduction ◽  
At Home

Background Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic. Objective This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference. Methods Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference. Results Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details. Conclusions The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.

scholarly journals Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers (Preprint)

2019 ◽  
Author(s):  
Reza Haghighi Osgouei ◽  
David Soulsby ◽  
Fernando Bello
Keyword(s):  
Machine Learning ◽  
Exercise Program ◽  
Machine Learning Algorithms ◽  
Qualitative Assessment ◽  
Shoulder Abduction ◽  
General View ◽  
Skeleton Model ◽  
Movement Data ◽  
P Movement ◽  
At Home

BACKGROUND Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic. OBJECTIVE This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference. METHODS Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference. RESULTS Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details. CONCLUSIONS The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.

scholarly journals Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1957
Author(s):  
Amandine Dubois ◽  
Titus Bihl ◽  
Jean-Pierre Bresciani
Keyword(s):  
Machine Learning ◽  
Fall Prevention ◽  
Fall Risk ◽  
Learning Algorithms ◽  
Step Length ◽  
Machine Learning Algorithms ◽  
Daily Activities ◽  
Sitting Time ◽  
Behavioral Parameters ◽  
At Home

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients’ movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians’ assessment toolkit and improve fall prevention.

scholarly journals Machine learning analysis to predict the need for ankle foot orthosis in patients with stroke

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoo Jin Choo ◽  
Jeoung Kun Kim ◽  
Jang Hwan Kim ◽  
Min Cheol Chang ◽  
Donghwi Park
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Machine Learning Algorithms ◽  
Shoulder Abduction ◽  
Stroke Patients ◽  
Ankle Foot Orthosis ◽  
Affected Side ◽  
Foot Orthosis ◽  
Ankle Dorsiflexor

AbstractWe investigated the potential of machine learning techniques, at an early stage after stroke, to predict the need for ankle–foot orthosis (AFO) in stroke patients. We retrospectively recruited 474 consecutive stroke patients. The need for AFO during ambulation (output variable) was classified according to the Medical Research Council (MRC) score for the ankle dorsiflexor of the affected limb. Patients with an MRC score of < 3 for the ankle dorsiflexor of the affected side were considered to require AFO, while those with scores ≥ 3 were considered not to require AFO. The following demographic and clinical data collected when patients were transferred to the rehabilitation unit (16.20 ± 6.02 days) and 6 months after stroke onset were used as input data: age, sex, type of stroke (ischemic/hemorrhagic), motor evoked potential data on the tibialis anterior muscle of the affected side, modified Brunnstrom classification, functional ambulation category, MRC score for muscle strength for shoulder abduction, elbow flexion, finger flexion, finger extension, hip flexion, knee extension, and ankle dorsiflexion of the affected side. For the deep neural network model, the area under the curve (AUC) was 0.887. For the random forest and logistic regression models, the AUC was 0.855 and 0.845, respectively. Our findings demonstrate that machine learning algorithms, particularly the deep neural network, are useful for predicting the need for AFO in stroke patients during the recovery phase.

An Empirical Evaluation of User Movement Data on Smartphones

2019 ◽  
Vol 18 (04) ◽  
pp. 1950025
Author(s):  
Christopher Kelley ◽  
Janelle Mason ◽  
Albert Esterline ◽  
Kaushik Roy
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Accelerometer Data ◽  
K Nearest Neighbor ◽  
Movement Data ◽  
User Movement

Movement data can be collected and used to add new security features and functionality to users’ mobile devices. Measuring a user’s movement using mobile devices allows for the use of behavioral biometrics. This assessment could introduce a shift in our current methods for securing mobile devices: instead of physical attributes like fingerprints or our face, the use of behavioral attributes like the way we walk or perform some personal activity. In this paper, an empirical evaluation of different classification techniques is conducted on user movement data. The datasets used in this empirical evaluation contain accelerometer data that were collected during various experiments from several mobile devices, including smartphones, smart watches, and other accelerometer sensors. We aggregated the user movement data and provided them as input into five traditional machine learning algorithms. The classification performances of the data were compared with a deep learning technique, the Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN). The LSTM-RNN achieved its highest accuracy at 89% compared to 97% from a traditional machine learning algorithm, specifically the k-Nearest Neighbor (k-NN) algorithm on wrist-worn accelerometer data, thus showing the LSTM to be a less viable option.

scholarly journals Outside in: exile at home an algorithmic discrimination system

Artnodes ◽  
2020 ◽  
Author(s):  
Annabel Castro
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Present Article ◽  
Daily Life ◽  
Machine Learning Algorithms ◽  
Indefinite Detention ◽  
The Right ◽  
Japanese Ancestry ◽  
At Home

Outside-in is an installation that utilises machine learning to reflect on systematic discrimination by focusing on the indefinite detention of Mexicans with Japanese heritage concentrated in Morelos during WWII. This algorithmic discrimination system tears apart four classic fiction films continuously within a projection room. The fragments are displaced and classified using machine learning algorithms. The system selects, separates and reassembles the fragments into new orders. It evokes the condition of being robbed of your right to be in the place to which you belong. The citizens detained during WWII were removed from their residence, their belongings were confiscated and they were placed in seclusion solely for having Japanese ancestry. Similarly, at present, data retrieving companies configure low resolution representations of ourselves from the snatched digital debris of our daily life. These pieces are reconfigured into archetypes and meaning is attached to them for massive decision making. We don’t have the right or means to know what these representations look like or what meaning has been attached to such shapes. It is a privilege reserved to the designers of algorithmic processes: they own this right and we the citizens own the consequences. The present article is a case study presenting the creation of Outside in: exile at home as an installation that utilises machine learning and reflects on this kind of systematic discrimination.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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