Detecting when something unusual has happened could help assistive robots operate more safely and effectively around people. However, the variability associated with people and objects in human environments can make anomaly detection difficult. We previously introduced an algorithm that uses a hidden Markov model (HMM) with a log-likelihood detection threshold that varies based on execution progress. We now present an improved version of our previous algorithm (HMM-D) and introduce a new algorithm based on Gaussian process regression (HMM-GP). We also present a new and more thorough evaluation of 8 anomaly detection algorithms with force, sound, and kinematic signals collected from a robot closing microwave doors, latching a toolbox, scooping yogurt, and feeding yogurt to able-bodied participants. Overall, HMM-GP had the highest performance in terms of area under the curve for these real-world tasks, and multiple modalities improved performance with some anomalies being better detected with particular modalities. With synthetic anomalies, HMM-D exhibited shorter detection delays and outperformed HMM-GP with high-magnitude anomalies. In general, higher-magnitude synthetic anomalies tended to be detected more rapidly.