A simple application of word2vec for activity modeling can be found here. We try to infer relative sensor locations from sequence of sensor triggerings. The true floor plan and the inferred sensor locations (for sensor ids starting with 'M' and 'MA') are shown below (download the data here). This demonstrates a form of 'embedding' of the sensors in a latent space. The premise is that the non-iid data such as activity sequences may be represented in the latent space as i.i.d data on which standard anomaly detectors may be employed. We can be a bit more creative and try to apply transfer learning with this embedding.
For example, imagine that we have a house (House-1) with labeled sensors (such as 'kitchen', 'living room', etc.) and another (House-2) with partially labeled sensors. Then, if we try to reduce the 'distance' between similarly labeled sensors in the latent space (by adding another loss-component to the word2vec embeddings), it can provide more information on which of the unlabeled sensors and activities in House-2 are similar to those in House-1. Moreover, the latent space allows representation of heterogeneous entities such as sensors, activities, locations, etc. in the same space which (in theory) helps detect similarities and associations in a more straightforward manner. In practice, the amount of data and the quality of the loss function matter a lot. Moreover, simpler methods of finding similarities/associations should not be overlooked. As an example, we might try to use embedding to figure out if a particular sensor is located in the bedroom. However, it might be simpler to just use the sensor's activation time to determine this information (assuming people sleep regular hours).
Please refer to the following paper and the CASAS website for the setup: D. Cook, A. Crandall, B. Thomas, and N. Krishnan. CASAS: A smart home in a box. IEEE Computer, 46(7):62-69, 2013.