Accuracy and repeatability of wrist joint angles in boxing using an electromagnetic tracking system

© 2019, The Author(s). The hand-wrist region is reported as the most common injury site in boxing. Boxers are at risk due to the amount of wrist motions when impacting training equipment or their opponents, yet we know relatively little about these motions. This paper describes a new method for quantifying wrist motion in boxing using an electromagnetic tracking system. Surrogate testing procedure utilising a polyamide hand and forearm shape, and in vivo testing procedure utilising 29 elite boxers, were used to assess the accuracy and repeatability of the system. 2D kinematic analysis was used to calculate wrist angles using photogrammetry, whilst the data from the electromagnetic tracking system was processed with visual 3D software. The electromagnetic tracking system agreed with the video-based system (paired t tests) in both the surrogate ( 0.9). In the punch testing, for both repeated jab and hook shots, the electromagnetic tracking system showed good reliability (ICCs > 0.8) and substantial reliability (ICCs > 0.6) for flexion–extension and radial-ulnar deviation angles, respectively. The results indicate that wrist kinematics during punching activities can be measured using an electromagnetic tracking system

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

A modular framework for modelling and verification of activities in ambient intelligent systems

There is a growing need to introduce and develop formal techniques for computational models capable of faithfully modelling systems of high complexity and concurrent. Such systems the are ambient intelligent systems. This article proposes an efficient framework for the automated modelling and verification of the behavioural models capturing daily activities that occur in ambient intelligent systems based on the modularity and compositionality of Petri nets. This framework consists of different stages that incorporate Petri net techniques like composition, transformation, unfolding and slicing. All these techniques facilitate the modelling and verification of the system activities under consideration by allowing the modelling in different Petri net classes and the verification of the produced models either by using model checking directly or by applying Petri net slicing to alleviate the state explosion problem that may emerge in very complex behavioural models. Illustrative examples are provided to demonstrate the practicality and effectiveness of the proposed approach. Finally, to show the flexibility of the proposed framework in terms of verification, both an evaluation and comparison of the state space required for the property checking are conducted with respect to the typical model checking and slicing approach respectively

Analysis of accelerometer data for personalised mood detection in activities of daily living

This paper proposes a novel approach to identify moods in Activities of Daily Living (ADLs) using accelerometer sensor data from 15 participants over 7 sessions each. Monitoring ADLs and detecting moods are of particular importance due to the potential life-changing consequences. The ADL considered relate to preparing and drinking a hot beverage, and they were segmented into four sub-activities: (i) entering kitchen, (ii) preparing beverage, (iii) drinking beverage, and (iv) exiting kitchen. The accelerometer was attached to the participants’ wrist, and prior to collecting the data, they were asked about their current mood. Two approaches were considered in the analysis according to the moods reported by the participants (happy, calm, tired, stressed, excited, sad, and bored), firstly using all trials, and secondly using a balanced sample of data. A set of statistical, temporal, and spectral features were extracted fromacceleration data, and personalised classification models were built and evaluated using the Random Forest algorithm. The experimental results showed that the average F-measure for all personalized classifiers was 0.75 (σ 0.20) considering all data, and 0.76 (σ 0.22) using balanced data. The best classification results were obtained with the “preparing” and “drinking” activities, and with the “happy”, “calm”, and “stressed” moods. This suggests that the use of accelerometers, such as those incorporated into smartwatches or activity trackers, may be useful in detecting moods in ADLs

Use of thermal sensor data for personalised mood detection in activities of daily living (ADLs)

Ambient sensors have been typically used in Human Activity Recognition (HAR) to monitor the activities of people and to detect unusual activities that may affect a person’s wellbeing. The main advantages of ambient sensors are that they are not intrusive and do not require the user to charge them periodically. Thermal sensors are a type of ambient sensor that provides temperature data from the environment in which they are placed, allowing to identify a thermal representation of elements that produce heat, such as people, animals or hot objects. In most cases, the focus of HAR research is on the physical health of people, not on their mental health. This paper presents an investigation on the use of thermal sensor data from people performing Activities of Daily Living (ADLs) to identify mood in a personalised way. Thermal data was collected from 15 participants performing the ADLs of preparing and drinking a hot beverage in 7 sessions. At the start of each session participants reported their mood. Classification results were produced for each participant using the Support Vector Machines (SVM) model in 10-Fold Cross Validation (CV) and in 80/20 split. The average accuracy values obtained of 0.9123 (80/20) and 0.9233 (CV), and of Cohen’s Kappa Coefficient of 0.8375 (80/20) and 0.8574 (CV) are promising for a thermal sensor personalised mood detection approach