Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions.

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Real-time human ambulation, activity, and physiological monitoring: taxonomy of issues, techniques, applications, challenges and limitations.

It is essential to increase the productivity of agricultural and farming processes to improve yields and cost-effectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest sub-vertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.

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Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks.

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Human activity recognition with inertial sensors using a deep learning approach

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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Deep Learning for Monitoring of Human Gait: A Review

Polymer optical fiber sensors in human life safety

We report on the photonic variant of the previously introduced Guided-Path Tomography (GPT), by demonstrating a system for footstep imaging using Plastic Optical Fiber (POF) sensors. The 1 m × 2 m sensor head is manufactured by attaching 80 POF sensors on a standard commercial carpet underlay. The sensing principle relies on the sensitivity of POF to bending, quantified by measuring light transmission. The Photonic GPT (PGPT) system, comprising the sensor head with processing hardware and software, covered by a mass-production general-purpose carpet top, successfully performs footstep imaging and correctly displays the position and footfall of a person walking on the carpet in real time. We also present the implementation of fast footprint “center of mass” calculations, suitable for recording gait and footfall. A split-screen movie, showing the frame-by-frame camera-captured action next to the reproduced footprints is included in the electronic version of this paper.

Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

Sub-surface modification by a femtosecond laser, producing pulses of 100fs duration at 400nm wavelength for fabrication of optical components is investigated, within sub-millimetre thick sheets of undoped polymethyl methacrylate (PMMA). For effective structuring, the influence of laser writing parameters and focusing conditions were studied to optimise the pulse energy, pulses spacing and focusing depth in the material. Permanent sub-surface refractive index structures with a minimum feature size (similar to 3 mu m) at a depth of similar to 500 mu m demonstrated the feasibility of writing flexible 3-D microstructures within a thin PMMA substrate. This investigation of focusing depth location in PMMA bulk material enabled photonic structures at the desired spatial resolution to be written within the core of single mode polymer optical fibre under controlled laser writing conditions. A long period fibre grating (LPG) consisting of a 2.8 mu m wide refractive index structure with a periodicity of 250 mu m and containing a series of attenuation bands in visible range, demonstrated a photonic structure written into a single mode polymer optical fibre.

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Writing parameters for 3D refractive index structures in polymethyl methacrylate using femtosecond radiation at 400 nm

We report first results on developing smart sensor systems for the automatic and frequent collection of animal weight and gait data, under the hostile conditions of a livestock farm. The novelty in our approach is to sense frequently the animals' floor contact, in suitably chosen locations, under natural and unobtrusive conditions. We demonstrate a pilot low profile rubberized mat sensor heads, delivering a large number of plastic optical fiber transmission measurements taken frequently from individual deformation sensors. The acquired data allows gait analysis from guided-path tomography images or by machine learning. We demonstrate weight estimation with accuracy better than 1% over a range suitable for deployment in pig farms.

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Floor sensors of animal weight and gait for precision livestock farming

A sensor for sweat or other aqueous discharge onto skin comprises a plastic optical fibre comprising a core and cladding, said core being in intimate contact at a first end with a light emitter and at a second end with a light detector, and said fibre having a sensing zone in which the cladding is replaced by a water-permeable translucent biocompatible polymer containing dispersed therein a biocompatible indicator that varies the intensity of an optical signal at a selected wavelength depending on whether the indicator is wet or dry. Such a sensor permits real time monitoring of sweating or other discharge, and can also enable electronic recording and/or remote monitoring.

J. Vaughan

Papers

Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

Writing parameters for 3D refractive index structures in polymethyl methacrylate using femtosecond radiation at 400 nm

Floor sensors of animal weight and gait for precision livestock farming

Fibre optic sweat sensor

Photonic Guided-Path Tomography with Fibre Transducers