DOSE: Detecting user-driven operating states of electronic devices from a single sensing point

Electricity and appliance usage information can often reveal the nature of human activities in a home. For instance, sensing the use of vacuum cleaner, a microwave oven, and kitchen appliances can give insights into a person´s current activities. Instead of putting a sensor on each appliance, our technique is based on the idea that appliance usage can be sensed by their manifestations in an environment´s existing electrical infrastructure. Prior approaches using this technique could only detect an appliance´s on-off states; that is, they only sense “what” is being used, but not “how” it is used. In this paper, we introduce DOSE, a significant advancement for inferring operating states of electronic devices from a single sensing point in a home. When an electronic device is in operation, it generates time-varying Electromagnetic Interference (EMI) based upon its operating states (e.g., vacuuming on a rug vs. hardwood floor). This EMI noise is coupled to the power line and can be picked up from a single sensing hardware attached to the wall outlet in a house. Unlike prior data-driven approaches, we employ domain knowledge of the device´s circuitry for semi-supervised model training to avoid tedious labeling process. We evaluated DOSE in a residential house for 2 months and found that operating states for 16 appliances could be estimated with an average accuracy of 93.8%. These fine-grained electrical characteristics affords rich feature sets of electrical events and have the potential to support various applications such as in-home activity inference, energy disaggregation and device failure detection.