The use of smartphones and wearables as sensing devices has created innumerable context inference apps including a class of workout tracking apps. Workout data generated by mobile tracking apps can assist both users and physicians in achieving better health care, rehabilitation, and self-motivation. Previous approaches impose extra burdens on users by requiring users to select types of exercises or to start/stop sessions. In this paper, we propose MiLift, a practical end-to-end workout tracking system that performs automatic segmentation to remove user burdens. MiLift uses commercial off-the-shelf smartwatches to accurately and efficiently track both cardio and weightlifting workouts without manual inputs from users. For weightlifting tracking, MiLift supports both machine-based and free weight exercises, and proposes a lightweight repetition detection algorithm to ensure efficiency. A research study of 22 users shows that MiLift can achieve above 90 percent average precision and recall for cardio workout classification, weightlifting session detection, and weightlifting type classification. MiLift can also count repetitions of weightlifting exercises with an average error of 1.12 reps (out of an average of 9.65). Our empirical app study on a Moto 360 watch suggests that MiLift can extend watch battery lives by up to 8.25χ (19.13h) compared with previous approaches.

The broad spectrum of interconnected sensors and actuators, available on various mobile devices and smartphones and collectively defined as the Internet of Things (IoT), have evolved into platforms with ability to both collect personal sensory data and also change the users' immediate environment. The continuous streams of richly annotated sensory data on these IoT devices have also enabled the emergence of a new class of context-aware apps that use the data to infer user context and accordingly customize their responses in real-time. However, this growth in the number of apps has not been complemented with adequate system support on the IoT devices resulting in monolithic apps that each implement and execute their own customized sensing pipelines. In this paper, we outline our vision of a sensing stack, akin to a networking stack, that can facilitate the development and execution of context-aware apps on IoT devices. There are several advantages to building a rich sensing stack. First, it allows apps to reuse stages of the sensing pipeline easing their development. Second, the layers of the stack allow for both in- and cross-layer resource optimization. Finally, it allows better control over the shared data as instead of raw-sensor data, higher-level semantic abstractions, such as inferences can now be shared with apps. We describe our initial efforts towards creating the different building blocks of such a sensing stack.

ABSTRACT By combining multiple factors during authentication, a service can provide better assurance of security. However, the users are likely to feel inconvenient, or even discard the service. This paper, therefore, addresses this issue and introduces a novel method, referred to as the Quantified riSk and Benefit adaptive Authentication Factors combination (QSBAF). QSBAF balances the requirements for both security and usability in the authentication of an information system and improves the system's ability to respond quickly to emerging risky events. In QSBAF, the authentication factors can be dynamically combined on the basis of quantified risk, benefit measurements, and combination policies. Furthermore, QSBAF provides an adaptive mechanism, which is driven by history data to justify the measurements of risk and benefit. In this paper, we use the online banking system as a typical scenario to demonstrate the usage of QSBAF. We also implement a prototype of QSBAF to evaluate the performance of its feasibility in real application scenarios. Copyright © 2013 John Wiley & Sons, Ltd.

We present MiDebug, a web-based Integrated Development Environment (IDE) for embedded system programming with in-browser debugging capabilities. This web application greatly reduces the time and effort required for rapid prototyping of microcontroller based devices.