Smart hospital emergency system via mobile-based requesting services

In recent years, the UK’s emergency call and response has shown elements of great strain as of today. The strain on emergency call systems estimated by a 9 million calls (including both landline and mobile) made in 2014 alone. Coupled with an increasing population and cuts in government funding, this has resulted in lower percentages of emergency response vehicles at hand and longer response times. In this paper, we highlight the main challenges of emergency services and overview of previous solutions. In addition, we propose a new system call Smart Hospital Emergency System (SHES). The main aim of SHES is to save lives through improving communications between patient and emergency services. Utilising the latest of technologies and algorithms within SHES is aiming to increase emergency communication throughput, while reducing emergency call systems issues and making the process of emergency response more efficient. Utilising health data held within a personal smartphone, and internal tracked data (GPU, Accelerometer, Gyroscope etc.), SHES aims to process the mentioned data efficiently, and securely, through automatic communications with emergency services, ultimately reducing communication bottlenecks. Live video-streaming through real-time video communication protocols is also a focus of SHES to improve initial communications between emergency services and patients. A prototype of this system has been developed. The system has been evaluated by a preliminary usability, reliability, and communication performance study

Examining sensor-based physical activity recognition and monitoring for healthcare using Internet of Things: A systematic review.

Due to importantly beneficial effects on physical and mental health and strong association with many rehabilitation programs, Physical Activity Recognition and Monitoring (PARM) have been considered as a key paradigm for smart healthcare. Traditional methods for PARM focus on controlled environments with the aim of increasing the types of identifiable activity subjects complete and improving recognition accuracy and system robustness by means of novel body-worn sensors or advanced learning algorithms. The emergence of the Internet of Things (IoT) enabling technology is transferring PARM studies to open and connected uncontrolled environments by connecting heterogeneous cost-effective wearable devices and mobile apps. Little is currently known about whether traditional PARM technologies can tackle the new challenges of IoT environments and how to effectively harness and improve these technologies. In an effort to understand the use of IoT technologies in PARM studies, this paper will give a systematic review, critically examining PARM studies from a typical IoT layer-based perspective. It will firstly summarize the state-of-the-art in traditional PARM methodologies as used in the healthcare domain, including sensory, feature extraction and recognition techniques. The paper goes on to identify some new research trends and challenges of PARM studies in the IoT environments, and discusses some key enabling techniques for tackling them. Finally, this paper consider some of the successful case studies in the area and look at the possible future industrial applications of PARM in smart healthcare

Bridge the Gap peer mentoring: reflections and actions

The Black and Asian Minority Ethnic (BAME) awarding gap refers to the significant disparity in degree class gained by BAME students compared to their white peers (Surridge, 2008; Singh, 2011; NUS, 2019). While the phenomena is well documented, there is limited understanding of why BAME students do not perform to their full potential.    Academic capability is strongly tied to feelings of belonging and fitting-in, and distinct differences between minority ethnic groups within the BAME category, in relation to factors that affect their engagement and attainment, are also highlighted by research (Connor et al., 2004; Dhanda, 2010).     The influence of peer support upon social integration and senses of belonging are well documented (Kauser et al., 2021).  As is peer mentoring in improving the success, retention and educational experiences of students in HE (Terrion and Leonard, 2007).  With this in mind, and to ensure that the individual voices of specific groups with the LJMU BAME student community were heard, we launched the Bridge the Gap Mentoring Programme in January 2022.     10 current LJMU students, home and international, were employed as project mentors. The programme focussed on understanding the ‘lived’ student experience at university, including what challenges they face in their day-to-day studies (progression) and how the curriculum, university environment and teaching and assessment approaches at LJMU affect their performance.  Mentors offered guidance and support to mentees, and gathered anonymised data about mentees’ experiences, successes and challenges.      In this workshop we present, in collaboration with project researchers and mentors, themes from mentoring sessions and lessons learned from engaging with BAME students, specifically around promotion and messaging. We invite participants to discuss and outline how we can embed peer mentoring across the institution, ensuring that hard-to-reach and at-risk students are able to benefit from semi-structured peer support that peer mentoring offers. At the same time, ensuring that we continue to develop a deep institutional understanding of the barriers that could affect academic performance and success. &nbsp

Towards fog driven IoT healthcare: challenges and framework of fog computing in healthcare

As we are within the era of the internet of things (IoT) its increasing integration to our everyday lives means that the devices involved produce massive amounts of data every second from billions of devices. The current approach used to handle this data is cloud computing. However because of its requirement of data centres this can become infeasible for the processing of data from IoT due to distance between these IoT smart objects (e.g., sensors) and the data centre. If this data holds any importance to minimal delay then the travel time between the end device and the clouds data centre could affect the relevance of that data. Therefore, to deal with these issues a new network paradigm placed closer to the IoT end devices is introduced called "Fog computing" to help address these challenges. If introduced effectively then fog computing can lead to the improvements in the quality of service (QoS) offered to systems that require the processing of delay sensitive data like healthcare systems that could benefit from the quick processing of data from sensors to allow the monitoring of patients. This paper has a main focus on healthcare systems. An architecture containing three layers; things (i.e., sensors), fog nodes and a cloud data centre is proposed alongside a framework incorporating this architecture. This framework offers collaboration among fog nodes with optimal management of resources and job allocation, which is able to achieve a high QoS (i.e., low latency) within the scenario of a healthcare system

Enabling high performance fog computing through fog-2-fog coordination model

Fog computing is a promising network paradigm in the IoT area as it has a great potential to reduce processing time for time-sensitive IoT applications. However, fog can get congested very easily due to fog resources limitations in term of capacity and computational power. In this paper, we tackle the issue of fog congestion through a request offloading algorithm. The result shows that the performance of fogs nodes can be increased be sharing fog's overload over several fog nodes. The proposed offloading algorithm could have the potential to achieve a sustainable network paradigm and highlights the significant benefits of fog offloading for the future networking paradigm

IoT-fog optimal workload via fog offloading

Billions of devises are expected to be connected to the Internet of Things network in the near future, therefore, a considerable amount of data will be generated, and gathered every second. The current network paradigm, which relies on centralised data-centres (a.k.a. Cloud computing), becomes impractical solution for IoT data due to the long distance between the data source and designated data-center. In other words, the amount of time taken by data to travel to a data-centre makes the importance of the data vanished. Therefore, the network topology have been evolved to permit data processing at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service via efficient and quick responding to sensors requests. In this paper, we are proposing a fog computing architecture and framework to enhance QoS via request offloading method. The proposed method employ a collaboration strategy among fog nodes in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of IoT requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of fog computing into the computing ecosystem

A diverse and multi-modal gait dataset of indoor and outdoor walks acquired using multiple cameras and sensors

Abstract Gait datasets are often limited by a lack of diversity in terms of the participants, appearance, viewing angle, environments, annotations, and availability. We present a primary gait dataset comprising 1,560 annotated casual walks from 64 participants, in both indoor and outdoor real-world environments. We used two digital cameras and a wearable digital goniometer to capture visual as well as motion signal gait-data respectively. Traditional methods of gait identification are often affected by the viewing angle and appearance of the participant therefore, this dataset mainly considers the diversity in various aspects (e.g., participants’ attributes, background variations, and view angles). The dataset is captured from 8 viewing angles in 45° increments along-with alternative appearances for each participant, for example, via a change of clothing. The dataset provides 3,120 videos, containing approximately 748,800 image frames with detailed annotations including approximately 56,160,000 bodily keypoint annotations, identifying 75 keypoints per video frame, and approximately 1,026,480 motion data points captured from a digital goniometer for three limb segments (thigh, upper arm, and head)

Fog computing framework for internet of things applications

Within the Internet of Things (IoT) era, a big volume of data is generated/gathered every second from billions of connected devices. The current network paradigm, which relies on centralised data centres (a.k.a. Cloud computing), becomes impractical solution for IoT data storing and processing due to the long distance between the data source (e.g., sensors) and designated data centres. In other words, by the time the data reaches a far data centre, the importance of the data would be vanished. Therefore, the network topologies have been evolved to permit data processing and storage at the edge of the network, introducing what so-called "Fog computing". The later will obviously lead to improvements in quality of service (QoS) via processing and responding quickly and efficiently to varieties of data processing requests. Therefore, understanding Fog computing architecture and its role in improving QoS is a paramount research topic. In this research, we are proposing a Fog computing architecture and framework to improve QoS for IoT applications. Proposed system supports cooperation among Fog nodes in a given location, in order to permit data processing in a shared mode, hence satisfies QoS and serves largest number of service requests. The proposed framework could have the potential in achieving sustainable network paradigm and highlights significant benefits of Fog computing into the computing ecosystem

ENHANCING DROUGHT TOLERANCE IN CAMELINA SATIVA L. AND CANOLA (BRASSICA NAPUS L.) THROUGH APPLICATION OF SELENIUM

Considering the burning issue the present study was undertaken in pot culture at the Cholistan Institute of Desert Studies (CIDS), The Islamia University of Bahawalpur, Pakistan for enhancing drought tolerance in two oilseed crops (OC) crops camelina (Camelina sativa L.) and canola (Canola napus L.) through improving physiological, biochemical, and antioxidants activity by foliar application of selenium (Se) under drought stress. Two Camelina (i.e., ‘Australian Camelina’ and ‘Canadian Camelina’) and canola genotypes (i.e., ‘AARI Canola’ and ‘UAF Canola’) were used as plant materials during the growing season of 2016. Both Camelina and Canola genotypes were grown under normal (100% FC) and water deficit (drought stress) (40% FC) conditions. Four levels of Se: such as seeds priming with 75μM Se, foliar application of Se at 7.06 μM and foliar Se in combination with seeds priming (7.06 μM & 75μM) along with control were applied at the vegetative stage of both OC crops for screening drought tolerant genotypes. All treatments were arranged three times in a randomized complete block design. Both OC crops were grown upto the maturity and data on physiochemical, antioxidants and yield components were recorded during this study. Results of the present study indicated that the physio-biochemical parameters such as WP (water potential), OP (osmotic potential), TP (turgor pressure), proline, TSS (total soluble sugar), TFAA (total free amino acids), TPr (total proteins) and TS (total sugars); and total chlorophyll contents were improved by foliar application Se along with seed priming by Se in both OC crops in both drought stress and non-stress (control) conditions. Similarly, osmoprotectants such as GB (Glycinebetaine), anthocyanin, TPC (total phenolic contents) and flavonoids; as well as antioxidants such as APX (ascorbate peroxidase), SOD (superoxide dismutase), POD (peroxidase) and CAT (catalase) were also showed better enhancement in both OC crops through foliar application in combination with seed priming with Se (7.06 μM & 75μM) under normal as well as water deficit (drought) conditions. Yield and its components i.e., branches plant-1 (no.), 1000-seed weight (g), seed and biological yield (t ha-1 ) of both OC crops were increased through foliar application in combination with seed priming by Se (7.06 μM & 75μM) under drought and non-drought stress conditions. Both camelina and Canola genotypes categorized based on all the above-mentioned parameters under the water deficit (drought stress) condition and foliar application of Se, the genotype ‘Canadian Camelina’ maintained the highest values for all these attributes. Therefore, it is revealed that foliar application in combination with seed priming by Se helps to improve drought tolerance of OC crops and also leads to an increase in the productivity of crops under drought stress. Among the genotypes, ‘Canadian Camelina’ performed the best when seeds of the genotypes were primed with Se in combination with foliar application of Se at the vegetative stage