This paper is intended to guide developers of wireless systems who are puzzled by the vast number of radio configuration parameters and options. We provide experimental data comparing power consumption of Bluetooth Low Energy (BLE), ZigBee and ANT protocols for a cyclic sleep scenario, in which a short-range and low-power wireless sensor node periodically sends a data packet to a remote 'hub' with intervening sleep intervals. Devices such as wearable health monitors often use this scenario when interfacing with a mobile phone-based hub. For all measured sleep intervals BLE achieved lower power consumption (10.1 uA, 3.3 V supply at 120 s interval), compared with ZigBee (15.7 uA), and ANT (28.2 uA). Most of the power consumption differences can be attributed to the time taken for a node to connect to the hub after waking up and the use of sleep between individual RF packets. For the three protocols we determined a sleep interval at which the tradeoff between power consumption and data rate is optimized.

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This work introduces a novel wearable olfactory display that provides just-in-time release of scents based on the physiological state of the wearer. The device can release up to three scents and passively captures subtle chest vibrations associated with the beating of the heart and respiration through clothes. BioEssence is controlled via a custom-made smartphone app that allows the creation of physiological rules to trigger different scents (e.g., when the heart rate is above 80 beats per minute, release lavender scent). The device is wireless and lightweight, and it is designed to be used during daily life, clipped on clothes around the sternum area or used as a necklace. We provide a description of the design and implementation of the prototype and potential use cases in the context of mental wellbeing.

In this paper we present WristFlex, an always-available onbody gestural interface. Using an array of force sensitive resistors (FSRs) worn around the wrist, the interface can distinguish subtle finger pinch gestures with high accuracy (>80%) and speed. The system is trained to classify gestures from subtle tendon movements on the wrist. We demonstrate that WristFlex is a complete system that works wirelessly in realtime. The system is simple and light-weight in terms of power consumption and computational overhead. WristFlex's sensor power consumption is 60.7 µW, allowing the prototype to potentially last more then a week on a small lithium polymer battery. Also, WristFlex is small and non-obtrusive, and can be integrated into a wristwatch or a bracelet. We perform user studies to evaluate the accuracy, speed, and repeatability. We demonstrate that the number of gestures can be extended with orientation data from an accelerometer. We conclude by showing example applications.

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