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In-bed motion detection and classification are important techniques that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this article, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. To detect movements, we have designed a feature that we refer to as Log-Peak, which can be extracted from load cell data that is collected through wireless links in an energy-efficient manner. After detection, we set out to achieve a precise body motion classification. Toward this goal, we define nine classes of movements, and design a machine learning algorithm using Support Vector Machine, Random Forest, and XGBoost techniques to classify a movement into one of nine classes. For every movement, we have extracted 24 features and used them in our model. This movement detection/classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. We have applied multiple tree topologies for each technique to reach their best results. After examining various combinations, we have achieved a final classification accuracy of 91.5%. This system can be used conveniently for long-term home monitoring.