Radar-based human activity recognition in crowded environments using regression approaches is addressed. Whereas previous research has focused on single activities and subjects, the problem of continuous activity recognition involving up to five individuals moving in arbitrary directions in an indoor area is introduced. To treat the problem, a regression-based approach is used, which offers innovative insights into creating robust and accurate systems for monitoring human activities.Novel approaches utilizing LSTM or CNN regression techniques with Linear Regression and Support Vector Machine regressor are compared on extracted features from radar data through the Histogram of Oriented Gradients and Principal Component Analysis. These approaches are rigorously evaluated by a Leave-One-Group-Out method, with performance assessed using common regression metrics such as the RMSE. The most promising outcomes were observed for crowds of three and five individuals, with respective RMSE of approximately 0.4 and 0.6. These results were primarily achieved by utilizing the micro-Doppler (µD) Spectrogram or range-Doppler data domain.@en