Understanding human activities is a newly emerging paradigm that is greatly involved in developing ubiquitous health care (u-Health) systems. The aim of these systems is to seamlessly gather knowledge about the patient's health and, after collecting knowledge, make suggestions to the patient according to his/her health profile. For this purpose, one of the most important ubiquitous communication trends is the smartphone, which has drawn the attention of both professionals and caregivers for monitoring the aging population, childcare, fall detection, and cognitive impairment. Recognizing human actions in a ubiquitous environment is very challenging and researchers have extensively investigated different methods to recognize human activities in the past decade. However, this field of research still needs further exploration in order to improve the accuracy and reduce the computational cost of these health care systems. Therefore, for expediting the existing system, this research work investigated a novel approach based on feature selection and classification. In the proposed work, sparse Bayesian multinomial logistic regression (SBMLR) is used for feature subset selection and a multiclass support vector machine (SVM) is adapted for the classification of six human daily activities (laying down, walking up stairs, walking down stairs, sitting, standing, and walking). For identifying the best features among the features returned by the SBMLR, a tuned threshold value is used for the selection of the features. Further, other classification algorithms including K-nearest neighbor, decision tree, and naive Bayes and different feature selection methods such as principal component analysis and random subset feature selection are also used for evaluation and comparison. The dataset used for testing is obtained from the UCI Machine Learning Repository. It is collected by using a smartphone embedded with an accelerometer and gyroscope. The experimental results show that the highest accuracy of 99.40% can be achieved by using the proposed method. Moreover, the paired sample two-tailed t-test over the significance level of 0.05 reveals that the performance difference between the proposed technique and a competing technique is statistically significant.