A multi-sensor dataset with annotated activities of daily living recorded in a residential setting.

SPHERE is a large multidisciplinary project to research and develop a sensor network to facilitate home healthcare by activity monitoring, specifically towards activities of daily living. It aims to use the latest technologies in low powered sensors, internet of things, machine learning and automated decision making to provide benefits to patients and clinicians. This dataset comprises data collected from a SPHERE sensor network deployment during a set of experiments conducted in the 'SPHERE House' in Bristol, UK, during 2016, including video tracking, accelerometer and environmental sensor data obtained by volunteers undertaking both scripted and non-scripted activities of daily living in a domestic residence.

rTLS: Secure and Efficient TLS Session Resumption for the Internet of Things.

In recent years, the Transport Layer Security (TLS) protocol has enjoyed rapid growth as a security protocol for the Internet of Things (IoT). In its newest iteration, TLS 1.3, the Internet Engineering Task Force (IETF) has standardized a zero round-trip time (0-RTT) session resumption sub-protocol, allowing clients to already transmit application data in their first message to the server, provided they have shared session resumption details in a previous handshake.

From Bits of Data to Bits of Knowledge-An On-Board Classification Framework for Wearable Sensing Systems.

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems.

On cross-language consonant identification in second language noise.

Speech perception in everyday conditions is highly affected by the presence of noise of a different nature. The presence of overlapping speakers is considered an especially challenging scenario, as it introduces both energetic and informational masking. The efficacy of the masking also depends on the familiarity with the language of both the target and masking stimuli.