Mobile app testing and evaluation requires exposing the app to a wide array of real world context conditions viz. location, sensor values, network conditions etc. Such comprehensive context conditions are difficult to create in a development environment on a real device, therefore, simulating them in a mobile emulator is a promising alternative. We present ContextMonkey, a framework for context generation in a mobile emulator. ContextMonkey can generate realistic context by leveraging traces in correlated and interdependent way from heterogenous sources: remote trace databases, models, and trace files. It eases the burden on developers to collect correlated traces, convert them to a common format, and feed them to the emulator in an orchestrated manner. We present examples demonstrating the simplicity and potential of ContextMonkey in app testing scenarios.

Accurate performance testing of mobile apps require comprehensive simulation of real world context in a mobile emulator viz. location, sensor values, network conditions etc. Existing mobile emulators support such simulation, however they lack the capability for automated generation of realistic, correlated, and dependent context traces. As a result, the burden of their generation and injection is left to the developers who have to create their own traces. This is not straightforward: there are heterogenous remote databases of traces, mathematical models and trace files can be used as well, but the trace values should be correlated, and traces have to be converted to a common format. We are developing ContextMonkey - a framework that addresses these concerns by leveraging context traces from databases such as FourSquare [1], OpenSignal [2], Google Street View [3]. It acts as a harness to mobile emulators, and aims at improving the efficiency of mobile app performance tests.

The handshake gesture is an important part of the social etiquette in many cultures. It lies at the core of many human interactions, either in formal or informal settings: exchanging greetings, offering congratulations, and finalizing a deal are all activities that typically either start or finish with a handshake. The automated detection of a handshake can enable wide range of pervasive computing scanarios; in particular, different types of information can be exchanged and processed among the handshaking persons, depending on the physical/logical contexts where they are located and on their mutual acquaintance. This paper proposes a novel handshake detection system based on body sensor networks consisting of a resource-constrained wrist-wearable sensor node and a more capable base station. The system uses an effective collaboration technique among body sensor networks of the handshaking persons which minimizes errors associated with the application of classification algorithms and improves the overall accuracy in terms of the number of false positives and false negatives.