The ability to monitor a person's level of daily activity can inform self-management of physical activity and assist in augmenting behavioral interventions. For older adults, the importance of regular physical activity is critical to reduce the risk of long-term disability. In this work, we present GeriActive, an application on the Amulet wrist-worn device that monitors in real time older adults' daily activity levels (low, moderate and vigorous), which we categorized using metabolic equivalents (METs). The app implements an activity-level detection model we developed using a linear Support Vector Machine (SVM). We trained our model using data from volunteer subjects (n=29) who performed common physical activities (sit, stand, lay down, walk and run) and obtained an accuracy of 94.3% with leave-one-subject-out (LOSO) cross-validation. We ran a week-long field study to evaluate the usability and battery life of the GeriActive system where 5 older adults wore the Amulet as it monitored their activity level. Their feedback showed that our system has the potential to be usable and useful. Our evaluation further revealed a battery life of at least 1 week. The results are promising, indicating that the app may be used for activity-level monitoring by individuals or researchers for health delivery interventions that could improve the health of older adults.

An increasing number of wearable devices support the execution of multiple third-party applications, increasing the functionality and flexibility of these devices. These multi-application, multi-tenant devices provide users with more options, and application developers with a standard platform. Typical ultra-low-power wearable devices, however, lack the type of hardware memory protection mechanisms~-- such as Memory Management Units (MMU)~-- needed to safely separate applications. At best, they provide a Memory Protection Unit (MPU), which allows the user to configure read/write/execute permissions for a few distinct regions of memory. At worst, no hardware memory protection is provided. MPU capabilities vary across hardware platforms, with many shortcomings: (1)~the MPU may only support a few distinct memory regions (fewer than one per application), (2)~the MPU may not protect all regions of memory, like hardware registers, and (3)~MPU protection boundary rules can be arcane, because they depend on opaque hardware implementations. Our key observation is that by supplementing a limited segment MPU with runtime checks, and using compile-time static analysis to explicitly layout applications in memory, we can guarantee application isolation (sandboxing) even on these limited MPUs, with lower overhead than software-only solutions.