9 skills found
AlessandraGalli / PPGCode to estimate HR from PPG signals using Subspace Decomposition and Kalman filter for the dataset of 22 PPG recordings provided for the 2015 IEEE Signal Processing Cup (SP Cup) competition. The traces are stored in folder 'DATABASE'. Please cite this publication when referencing this material: "Measuring Heart Rate During Physical Exercise by Subspace Decomposition and Kalman Smoothing", IEEE Transaction on Instrumentation & Measurement.
mintisan / Hr Estimatehr_estimate
TheAndreyZakharov / Compass HR AI Module📊📈 COMPASS-HR: AI-powered module for career paths, skill-gap analysis & org competency mapping. Recommends learning plans, helps build teams, models future roles, estimates hiring costs and aligns people development with strategy. Connects via open APIs to HR/ERP systems and uses Russian labor market data.
SayeedChowdhury / Heart Rate Estimation Using PPG During Exercise In Intense Motion ArtifactsThis project addresses to estimate heart rate (HR) during exercise in real-time using wrist-type PPG signals amidst intense motion artifacts
pgalko / Aerobic Threshold Estimation From DFA Alpha1A Python script that estimates aerobic threshold HR from activity HRV data. The script iterates through a list of activities and computes DFA Alpha 1 for each, then computes HR from alpha 1 (0.75) and plots the results. Takes a while to run.
ieeeWang / Estimate HR And HRV From Raw ECGA MATLAB project for estimating HR and HRV parameters from raw ECG signals
rlba96 / Energy ExpenditureEstimate real-time energy expenditure values by measuring multiple physiological variables (HR, acc, temp, gsr...)
Maksym-Bondarenko / AmIDrunkPet-project on estimating the level of drunkness, based on the rPPG signal (HR, HRV, etc.), redness of eyes and other facial signals. Further planned to add ML
Priyankagopale / Employee Turnover Machine Learning ProjectEmployee Turnover is one of the key market challenges in Human Resource (HR) Analytics. Organizations usually invest a greater amount of money and time in the hiring of staff and nursing them in the hope to receive value addition. When an employee leaves the company, the reduction of opportunity costs is borne by the company. Turnover is especially prevalent in large-scale recruitment agencies. The risk of replacing workers remains important for most employers. This is due to the amount of time spent recruiting and selecting a successor, the sign-on incentives, and the lack of morale for several months as the new employee gets used to the new job. The tangible costs of workforce turnover will be the cost of recruiting new staff, the cost of recruitment and hiring, the time of transition, future product or service quality issues, the cost of temporary staff, the cost of training, the cost of lack of production, the cost of lost expertise and the cost of the job being empty before an acceptable replacement is found. We find that the attributes of workers such as Job Position, overtime, work level affect significantly attrition. Various classification methods are introduced such as logistic regression, linear discriminate analysis, ridge classification, lasso classification, decision trees, random forests to forecast and concurrently measure the likelihood of turnover of every new employee. Data from an HR department of the company available at Kaggle were used to estimate the employee turnover. The dataset includes 10 different attributes of 1470 personnel. Dataset specifies if the personnel is leaving or staying based on the attributes. Now, to construct a prediction model based on the previously mentioned machine learning algorithms with 90 percent of the total personnel's attributes and the rest for model testing. The best performing performance algorithm yields the best accuracy of Decision Tree Classifier is 93 percent and the worst accuracy of Logistic Regression is 0.18%
