scholarly journals Human Movement Monitoring and Analysis for Prehabilitation Process Management

2020 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Khalid Al-Naime ◽  
Adnan Al-Anbuky ◽  
Grant Mawston
Keyword(s):  
Fourier Analysis ◽  
High Intensity ◽  
Hospital Setting ◽  
Human Movement ◽  
Dominant Frequency ◽  
Sensor Data ◽  
Moderate Intensity ◽  
Activity Levels ◽  
Accelerometer Data ◽  
Accelerometer Sensor

Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity.

Ambulatory Activity in People with Early Parkinson's Disease

2015 ◽  
Vol 17 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Robyn M. Lamont ◽  
Meg E. Morris ◽  
Marjorie H. Woollacott ◽  
Sandra G. Brauer
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Executive Function ◽  
Disease Severity ◽  
High Intensity ◽  
Community Dwelling ◽  
Cognitive Capacity ◽  
Moderate Intensity ◽  
Activity Levels ◽  
Ambulatory Activity

People with Parkinson's disease (PD) are encouraged to participate in physical activity levels equivalent to those recommended for the general population. Understanding factors that influence this activity is important for facilitating this participation. This study examined factors associated with participation in moderate and high intensity daily ambulatory activity in people with mild to moderate PD. Fifty community-dwelling people with mild-moderate PD were monitored with accelerometers over three days to characterise their daily ambulatory activity levels. Personal factors, disease characteristics, gait and cognitive capacity were measured. Prediction models were created to identify factors influencing ambulation activity. People with PD spent approximately 77 minutes walking per day, mostly at a moderate intensity resulting in a median of 6300 steps/day. Disease severity predicted time spent in moderate ambulation bouts (R2 = 0.116, p = .017). Gait (Timed Up and Go (TUG) Test) and executive function together predicted engagement in high intensity ambulatory activity (R2 > 0.170, p < .022). While disease severity, gait performance and executive function were predictive of engagement in moderate and high intensity walking activity, additional personal and social factors should be considered and are likely to also strongly impact on activity levels.

scholarly journals Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 174
Author(s):  
Junhyuk Kang ◽  
Jieun Shin ◽  
Jaewon Shin ◽  
Daeho Lee ◽  
Ahyoung Choi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Signal ◽  
Short Term Memory ◽  
Learning Model ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Accelerometer Sensor ◽  
Skeleton Image ◽  
Deep Learning Model

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

scholarly journals Motion Type Verification Studies Using Accelerometer Sensor Data With Local Mean Decomposition

2019 ◽  
Vol 2 (3) ◽  
pp. 1051-1057
Author(s):  
Mustafa Yasin Esas ◽  
Fatma Latifoğlu
Keyword(s):  
Human Physiology ◽  
Cell Phone ◽  
High Accuracy ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Accelerometer Sensor ◽  
Local Mean Decomposition ◽  
A Cell ◽  
Local Mean ◽  
High Degree

It is a significant improvement that the physical movements directly related to human physiology can be detected with high accuracy using sensors. In our study, three-axis accelerometer data recorded using a cell phone sensor in a controlled manner were used. Validation of walking, jogging, up-stairs, down-stair movements is aimed. For this purpose, local mean decomposition (LMD) function was used. The axis (x, y, z) in which the orthogonality value obtained from LMD was high was determined. Then, it was evaluated that there is movement in the direction of high value axis. While there is a high degree of accuracy in up-stair, down-stair and jogging movements, the desired success in walking movement was not achieved.

scholarly journals What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6816
Author(s):  
Seer J. Ikurior ◽  
Nelly Marquetoux ◽  
Stephan T. Leu ◽  
Rene A. Corner-Thomas ◽  
Ian Scott ◽  
...  
Keyword(s):  
Animal Health ◽  
Sensor Data ◽  
Diel Activity ◽  
Parasitic Nematodes ◽  
Accelerometer Data ◽  
Triaxial Accelerometer ◽  
Activity Budgets ◽  
Accelerometer Sensor ◽  
Ewe Lambs ◽  
The Impact

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.

A Comparison of a Moderate with Moderate-high Intensity Oral Anticoagulant Treatment in Patients with Mechanical Heart Valve Prostheses

1997 ◽  
Vol 77 (05) ◽  
pp. 0839-0844 ◽  
Author(s):  
Vittorio Pengo ◽  
Fabio Barbero ◽  
Alberto Banzato ◽  
Elisabetta Garelli ◽  
Franco Noventa ◽  
...  
Keyword(s):  
Heart Valve ◽  
High Intensity ◽  
Oral Anticoagulant ◽  
Oral Anticoagulants ◽  
Mechanical Heart Valve ◽  
Moderate Intensity ◽  
Minor Bleeding ◽  
Anticoagulant Treatment ◽  
Oral Anticoagulant Treatment ◽  
Valve Prostheses

SummaryBackground. The long-term administration of oral anticoagulants to patients with mechanical heart valve prostheses is generally accepted. However, the appropriate intensity of oral anticoagulant treatment in these patients is still controversial.Methods and Results. From March 1991 to March 1994, patients referred to the Padova Thrombosis Center who had undergone mechanical heart valve substitution at least 6 months earlier were randomly assigned to receive oral anticoagulants at moderate intensity (target INR = 3) or moderate-high intensity (target INR = 4). Principal end points were major bleeding, thromboembolism and vascular death. Minor bleeding was a secondary end-point.A total of 104 patients were assigned to the target 3 group and 101 to the target 4 group; they were followed for from 1.5 years to up 4.5 years (mean, 3 years). Principal end-points occurred in 13 patients in the target 3 group (4 per 100 patient-years) and in 20 patients in the target 4 group (6.9 per 100 patient-years). Major hemorrhagic events occurred in 15 patients, 4 in the target 3 group (1.2 per 100 patient-years) and 11 in the target 4 group (3.8 per 100 patient-years) (p = 0.019). The 12 recorded episodes of thromboembolism, 4 of which consisted of a visual deficit, were all transient ischemic attacks, 6 in the target 3 group (1.8 per 100 patient-years) and 6 in the target 4 group (2.1 per 100 patient- years). There were 3 vascular deaths in each group (0.9 and 1 per 100 patient-years for target 3 and target 4 groups, respectively). Minor bleeding episodes occurred 85 times (26 per 100 patient-years) in the target 3 group and 123 times (43 per 100 patient-years) in the target 4 group (p = 0.001).Conclusions. Mechanical heart valve patients on anticoagulant treatment who had been operated on at least 6 months earlier experienced fewer bleeding complications when maintained on a moderate intensity regimen (target INR = 3) than those on a moderate-high intensity regimen (target INR = 4). The number of thromboembolic events and vascular deaths did not differ between the two groups.

Effects of High Intensity Statin Therapy in the Treatment of Diabetic Dyslipidemia in Patients with Coronary Artery Disease

2018 ◽  
Vol 24 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Marija Vavlukis ◽  
Sasko Kedev
Keyword(s):  
Statin Therapy ◽  
High Intensity ◽  
Statistical Significance ◽  
Incident Diabetes ◽  
Moderate Intensity ◽  
Protective Effects ◽  
Pcsk9 Inhibitors ◽  
Diabetic Dyslipidemia ◽  
Patients With Diabetes

Background: Diabetic dyslipidemia has specifics that differ from dyslipidemia in patients without diabetes, which contributes to accelerated atherosclerosis equally as dysglycemia. The aim of this study was to deduce the interdependence of diabetic dyslipidemia and cardiovascular diseases (CVD), therapeutic strategies and the risk of diabetes development with statin therapy. Method: We conducted a literature review of English articles through PubMed, PubMed Central and Cochrane, on the role of diabetic dyslipidemia in atherosclerosis, the antilipemic treatment with statins, and the role of statin therapy in newly developed diabetes, by using key words: atherosclerosis, diabetes mellitus, diabetic dyslipidemia, CVD, statins, nicotinic acid, fibrates, PCSK9 inhibitors. Results: hyperglycemia and dyslipidemia cannot be treated separately in patients with diabetes. It seems that dyslipidemia plays one of the key roles in the development of atherosclerosis. High levels of TG, decreased levels of HDL-C and increased levels of small dense LDL- C particles in the systemic circulation are the most specific attributes of diabetic dyslipidemia, all of which originate from an inflated flux of free fatty acids occurring due to the preceding resistance to insulin, and exacerbated by elevated levels of inflammatory adipokines. Statins are a fundamental treatment for diabetic dyslipidemia, both for dyslipidemia and for CVD prevention. The use of statin treatment with high intensity is endorsed for all diabetes-and-CVD patients, while a moderate - intensity treatment can be applied to patients with diabetes, having additional risk factors for CVD. Statins alone are thought to possess a small, although of statistical significance, risk of incident diabetes, outweighed by their benefits. Conclusion: As important as hyperglycemia and glycoregulation are in CVD development in patients with diabetes, diabetic dyslipidemia plays an even more important role. Statins remain the cornerstone of antilipemic treatment in diabetic dyslipidemia, and their protective effects in CVD progression overcome the risk of statin- associated incident diabetes.

scholarly journals Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

2021 ◽  
pp. 158-166
Author(s):  
Noah Balestra ◽  
Gaurav Sharma ◽  
Linda M. Riek ◽  
Ania Busza
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Sensor Data ◽  
Inpatient Setting ◽  
Accelerometer Data ◽  
Data Set ◽  
Machine Learning Classification ◽  
Exercise Type ◽  
Exercise Dose ◽  
Rehabilitation Exercises

<b><i>Background:</i></b> Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. <b><i>Objectives:</i></b> The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. <b><i>Methods:</i></b> MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. <b><i>Results:</i></b> We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. <b><i>Conclusions:</i></b> Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

scholarly journals Habitual Physical Activity and Diabetes Control in Young and Older Adults with Type II Diabetes: A Longitudinal Correlational Study

2021 ◽  
Vol 18 (3) ◽  
pp. 1330
Author(s):  
Chia-Hsun Chang ◽  
Ching-Pyng Kuo ◽  
Chien-Ning Huang ◽  
Shiow-Li Hwang ◽  
Wen-Chun Liao ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Type Ii Diabetes ◽  
Daily Physical Activity ◽  
Diabetes Control ◽  
Moderate Intensity ◽  
Correlational Study ◽  
Activity Levels ◽  
Daily Physical Activities ◽  
Activity Questionnaire

This study aimed to determine whether daily physical activity in young and older adults with T2DM is associated with diabetes control. A prospective correlational study involving 206 young (≤65 years) and older (>65 years) adults was conducted. The International Physical Activity Questionnaire was used to assess their daily physical activity levels. Patients’ mean HbA1c level was 7.8% (±1.4), and 95.9% of patients had unsatisfactory diabetes control. Performing more minutes per week of moderate-intensity daily physical activity was associated with a lower risk of glycemia in both young and older adults. Furthermore, moderate daily physical activity significantly lowered the risk of glycemia. Health personnel must encourage patients to engage in moderate daily physical activities to improve diabetes control.

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