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Abstract: With the recent development of ubiquitous technologies, many new applications have been emerging for smart home implementation. Usually, such applications are based on diverse sensors. One fundamental operation in the applications is to find out semantically meaningful events or activities from huge sensor data stream. Usually, such event or activity is represented by a salient sequence pattern. Among the diverse research issues, detecting salient sequence patterns of human motions from image sensor data stream has received much attention for security and surveillance purposes. In the case of detecting human motions from image sensor data, finding and matching their salient sequence patterns could become more complicated since semantically same motions could show diverse variations such as different motion time. Based on this observation, in this paper, we propose a new querying and answering scheme for continuous sensor data stream to detect abnormal human motions. More specifically, we first present a new hierarchical querying scheme to consider variable length of semantically same human motions. Secondly, we present an indexing scheme to efficiently find semantically meaningful motion sequences in the sensor data stream. Thirdly, we present Dynamic Group Warping algorithm to effectively filter out unnecessary human motions. Through extensive experiments, we show that our proposed method achieves outstanding performance. [Copyright &y& Elsevier]