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. Trained annotators provided ground-truth labels annotating posture, ambulation, activity and location. This dataset is a valuable resource both within and outside the machine learning community, particularly in developing and evaluating algorithms for identifying activities of daily living from multi-modal sensor data in real-world environments. A subset of this dataset was released as a machine learning competition in association with the European Conference on Machine Learning (ECML-PKDD 2016).

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. Since it is common for IoT devices to transmit periodic messages to a server, this 0-RTT protocol can help in reducing bandwidth overhead. Unfortunately, the sub-protocol has been designed for the Web and is susceptible to replay attacks. In our previous work, we adapted the 0-RTT protocol to strengthen it against replay attacks, while also reducing bandwidth overhead, thus making it more suitable for IoT applications. However, we did not include a formal security analysis of the protocol. In this work, we address this and provide a formal security analysis using OFMC. Further, we have included more accurate estimates on its performance, as well as making minor adjustments to the protocol itself to reduce implementation ambiguity and improve resilience.

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. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.

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. This work analyses consonant identification by non-native English speakers in N-talker natural babble noise and babble-modulated noise, by varying the number of talkers in the babble. In particular, only English consonants that are also present in all the native languages of the subjects are used. As the subjects are familiar with the consonants used, this study can be considered a step towards a deeper analysis on perception of first language speech in the presence of second language maskers.