scholarly journals Tracking and Monitoring Fitness of Athletes Using IoT Enabled Wearables for Activity Recognition and Random Forest Algorithm for Performance Prediction

2021 ◽  
pp. 72-86
Author(s):  
Krishna Prasad K. ◽  
P. S. Aithal ◽  
Geetha Poornima K. ◽  
Vinayachandra
Keyword(s):  
Random Forest ◽  
Conceptual Model ◽  
Activity Recognition ◽  
Performance Prediction ◽  
Emotional Health ◽  
Wearable Devices ◽  
Physical Activities ◽  
Smart Devices ◽  
Related Services ◽  
And Performance

Purpose: The progression in technology is made the best use of in every field. Sports analytics is an essential sector that has gained importance in this technology-driven era. It is used to determine the hidden relationships among different quantitative parameters that affect the performance of athletes. This type of analysis requires a large amount of data to be stored periodically. Cloud acts as a scalable centralized repository that can store the massive data essential for analysis purpose. From the technological perspective there are numerous wearable activity tracking devices, which will be able to provide feedback of physical activities. With the help of random forest (RF) algorithm it is possible to classify huge datasets to perform predictions. In this paper, different smart devices that can be used to measure physical activity, use of RF algorithm for converting data obtained from smart devices into knowledge are explored. A conceptual model that uses wearable devices for tracking and monitoring and RF algorithm to predict the performance is suggested. Methodology: The study was conducted by referring to scholarly documents available online and by referring to websites of companies offering healthcare and sports related services. A conceptual model is developed based on the theoretical perception that incorporates the components needed for measuring the physical activities to predict the performance of athletes. Findings/Result: In this paper the proposed system contains four major activities as Capture, Store, Analyze, and Predict. The model considers use of IoT-enabled wearable devices to measure the physical activities of athletes and the information collected will in turn be used to analyze predict their performance and suggest them how to increase the chances of winning. However, the outcome of a game does not only depend upon the PA of athletes. It depends also upon the physical, mental, emotional health, nutrition and many other factors. Originality: In this paper, a theoretical model is deduced to integrate IoT and RF Algorithm to track and monitor fitness of athletes using wearables for activity recognition and performance prediction. Paper Type: Conceptual Paper

scholarly journals Random forest algorithms for recognizing daily life activities using plantar pressure information: a smart-shoe study

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10170
Author(s):  
Dian Ren ◽  
Nathanael Aubert-Kato ◽  
Emi Anzai ◽  
Yuji Ohta ◽  
Julien Tripette
Keyword(s):  
Random Forest ◽  
Activity Recognition ◽  
Plantar Pressure ◽  
Flat Surface ◽  
Daily Life ◽  
Smart Devices ◽  
Pressure Data ◽  
Process Data ◽  
Daily Life Activities ◽  
Daily Physical Activities

Background Wearable activity trackers are regarded as a new opportunity to deliver health promotion interventions. Indeed, while the prediction of active behaviors is currently primarily relying on the processing of accelerometer sensor data, the emergence of smart clothes with multi-sensing capacities is offering new possibilities. Algorithms able to process data from a variety of smart devices and classify daily life activities could therefore be of particular importance to achieve a more accurate evaluation of physical behaviors. This study aims to (1) develop an activity recognition algorithm based on the processing of plantar pressure information provided by a smart-shoe prototype and (2) to determine the optimal hardware and software configurations. Method Seventeen subjects wore a pair of smart-shoe prototypes composed of plantar pressure measurement insoles, and they performed the following nine activities: sitting, standing, walking on a flat surface, walking upstairs, walking downstairs, walking up a slope, running, cycling, and completing office work. The insole featured seven pressure sensors. For each activity, at least four minutes of plantar pressure data were collected. The plantar pressure data were cut in overlapping windows of different lengths and 167 features were extracted for each window. Data were split into training and test samples using a subject-wise assignment method. A random forest model was trained to recognize activity. The resulting activity recognition algorithms were evaluated on the test sample. A multi hold-out procedure allowed repeating the operation with 5 different assignments. The analytic conditions were modulated to test (1) different window lengths (1–60 seconds), (2) some selected sensor configurations and (3) different numbers of data features. Results A window length of 20 s was found to be optimum and therefore used for the rest of the analysis. Using all the sensors and all 167 features, the smart shoes predicted the activities with an average success of 89%. “Running” demonstrated the highest sensitivity (100%). “Walking up a slope” was linked with the lowest performance (63%), with the majority of the false negatives being “walking on a flat surface” and “walking upstairs.” Some 2- and 3-sensor configurations were linked with an average success rate of 87%. Reducing the number of features down to 20 does not alter significantly the performance of the algorithm. Conclusion High-performance human behavior recognition using plantar pressure data only is possible. In the future, smart-shoe devices could contribute to the evaluation of daily physical activities. Minimalist configurations integrating only a small number of sensors and computing a reduced number of selected features could maintain a satisfying performance. Future experiments must include a more heterogeneous population.

scholarly journals Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2216 ◽  
Author(s):  
Abdul Rehman Javed ◽  
Muhammad Usman Sarwar ◽  
Suleman Khan ◽  
Celestine Iwendi ◽  
Mohit Mittal ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Recognition ◽  
Physical Activities ◽  
Smart Devices ◽  
Accelerometer Sensor ◽  
Daily Life Activities ◽  
Adaptive Services ◽  
Physical Activity Recognition ◽  
Active Research ◽  
Mlp Classifier

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

scholarly journals Wearable Technologies in Field Hockey Competitions: A Scoping Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5242
Author(s):  
Jolene Ziyuan Lim ◽  
Alexiaa Sim ◽  
Pui Wah Kong
Keyword(s):  
Heart Rate ◽  
Inertial Sensors ◽  
Wearable Devices ◽  
Field Hockey ◽  
Heart Rate Monitors ◽  
Search Terms ◽  
Physiological Demands ◽  
And Performance ◽  
Future Work ◽  
Skin Temperatures

The aim of this review is to investigate the common wearable devices currently used in field hockey competitions, and to understand the hockey-specific parameters these devices measure. A systematic search was conducted by using three electronic databases and search terms that included field hockey, wearables, accelerometers, inertial sensors, global positioning system (GPS), heart rate monitors, load, performance analysis, player activity profiles, and competitions from the earliest record. The review included 39 studies that used wearable devices during competitions. GPS units were found to be the most common wearable in elite field hockey competitions, followed by heart rate monitors. Wearables in field hockey are mostly used to measure player activity profiles and physiological demands. Inconsistencies in sampling rates and performance bands make comparisons between studies challenging. Nonetheless, this review demonstrated that wearable devices are being used for various applications in field hockey. Researchers, engineers, coaches, and sport scientists can consider using GPS units of higher sampling rates, as well as including additional variables such as skin temperatures and injury associations, to provide a more thorough evaluation of players’ physical and physiological performances. Future work should include goalkeepers and non-elite players who are less studied in the current literature.

Experimental and computer aided solubility quantification of diverse lignins and performance prediction

2021 ◽  
Vol 57 (14) ◽  
pp. 1782-1785
Author(s):  
Olumoye Ajao ◽  
Marzouk Benali ◽  
Naïma El Mehdi
Keyword(s):  
Performance Prediction ◽  
Solvent Fractionation ◽  
Computer Aided ◽  
And Performance

New insights on the variability of solubility elucidated for diverse lignins, quantification thereby makes it possible to predict performance for solvent fractionation processes and polymers formulation.

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