Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures. In this paper, we suggest a novel method for online signature verification that relies on the increasingly available hand-worn devices, such as smartwatches or fitness trackers, instead of dedicated ad-hoc devices. Our method uses a set of known genuine and forged signatures, recorded using the motion sensors of a hand-worn device, to train a machine learning classifier. Then, given the recording of an unknown signature and a claimed identity, the classifier can determine whether the signature is genuine or forged. In order to validate our method, it was applied on 1980 recordings of genuine and forged signatures that we collected from 66 subjects in our institution. Using our method, we were able to successfully distinguish between genuine and forged signatures with a high degree of accuracy (0.98 AUC and 0.05 EER).
In this paper, we propose four continuous authentication designs by using the characteristics of arm movements while individuals walk. The first design uses acceleration of arms captured by a smartwatch's accelerometer sensor, the second design uses the rotation of arms captured by a smartwatch's gyroscope sensor, third uses the fusion of both acceleration and rotation at the feature-level and fourth uses the fusion at score-level. Each of these designs is implemented by using four classifiers, namely, k nearest neighbors (k-NN) with Euclidean distance, Logistic Regression, Multilayer Perceptrons, and Random Forest resulting in a total of sixteen authentication mechanisms. These authentication mechanisms are tested under three different environments, namely an intra-session, inter-session on a dataset of 40 users and an inter-phase on a dataset of 12 users. The sessions of data collection were separated by at least ten minutes, whereas the phases of data collection were separated by at least three months. Under the intra-session environment, all of the twelve authentication mechanisms achieve a mean dynamic false accept rate (DFAR) of 0 and dynamic false reject rate (DFRR) of 0 . For the inter-session environment, feature level fusion-based design with classifier k-NN achieves the best error rates that are a mean DFAR of 2.2 and DFRR of 4.2 . The DFAR and DFRR increased from 5.68 and 4.23 to 15.03 and 14.62 respectively when feature level fusion-based design with classifier k-NN was tested under the inter-phase environment on a dataset of 12 users.  In order for objects to perform personalized or contextual functions based on identity, they must solve what we call the object user identification problem: understanding who is actually using them. In this paper, we propose a new technique that uses data from wearable wrist sensors to perform object user identification. We hypothesize that objects have unique hallmarks that are imprinted in the hand gestures of its users. By detecting the presence of an object's hallmark in the wrist sensor data, we can identify who used the object. We evaluate this concept with a smart home application: recognizing who is using an object or appliance in a multi-person home by combining smart meter data and wearables. We conduct three different studies with 10 participants: 1) a study with scripted object use 2) a study with high-level tasked activities and unscripted object use, and 3) a 5-day in-situ study. These studies indicate that our approach performs object user identification with an average accuracy of 85--90 .