Smartphone and Portable Media Device: A Novel Pathway toward the Diagnostic Characterization of Human Movement

The application of wearable and wireless systems offers the capacity to ameliorate considerable strain on medical resources. In particular the smartphone and portable media device for quantifying human movement characteristics offers the opportunity to evaluate patients in a homebound environment remote from clinical resources and post-processing. Trial data can be easily transmitted as an email attachment with wireless connectivity to the Internet. The utility of the smartphone and portable media device has been demonstrated for quantifying gait, tendon reflex response, movement disorder, and rehabilitation exercise. Further evolution and potential has been demonstrated through the integration of machine learning to provide classification accuracy for differentiating between disparate human movement scenarios. The role of the smartphone and portable media device for quantifying human movement characteristics is further elucidated

An Evolutionary Perspective for Network Centric Therapy through Wearable and Wireless Systems for Reflex, Gait, and Movement Disorder Assessment with Machine Learning

Wearable and wireless systems have progressively evolved to achieve the capabilities of Network Centric Therapy. Network Centric Therapy comprises the application of wearable and wireless inertial sensors for the quantification of human movement, such as reflex response, gait, and movement disorders, with machine learning classification representing advanced diagnostics. With wireless access to a functional Cloud computing environment Network Centric Therapy enables subjects to be evaluated at any location of choice with Internet connectivity and expert medical post-processing resources situated anywhere in the world. The evolutionary origins leading to the presence of Network Centric Therapy are detailed. With the historical perspective and state of the art presented, future concepts are addressed

Multimodal Wearable Sensors for Human-Machine Interfaces

Certain areas of the body, such as the hands, eyes and organs of speech production, provide high-bandwidth information channels from the conscious mind to the outside world. The objective of this research was to develop an innovative wearable sensor device that records signals from these areas more conveniently than has previously been possible, so that they can be harnessed for communication. A novel bioelectrical and biomechanical sensing device, the wearable endogenous biosignal sensor (WEBS), was developed and tested in various communication and clinical measurement applications. One ground-breaking feature of the WEBS system is that it digitises biopotentials almost at the point of measurement. Its electrode connects directly to a high-resolution analog-to-digital converter. A second major advance is that, unlike previous active biopotential electrodes, the WEBS electrode connects to a shared data bus, allowing a large or small number of them to work together with relatively few physical interconnections. Another unique feature is its ability to switch dynamically between recording and signal source modes. An accelerometer within the device captures real-time information about its physical movement, not only facilitating the measurement of biomechanical signals of interest, but also allowing motion artefacts in the bioelectrical signal to be detected. Each of these innovative features has potentially far-reaching implications in biopotential measurement, both in clinical recording and in other applications. Weighing under 0.45 g and being remarkably low-cost, the WEBS is ideally suited for integration into disposable electrodes. Several such devices can be combined to form an inexpensive digital body sensor network, with shorter set-up time than conventional equipment, more flexible topology, and fewer physical interconnections. One phase of this study evaluated areas of the body as communication channels. The throat was selected for detailed study since it yields a range of voluntarily controllable signals, including laryngeal vibrations and gross movements associated with vocal tract articulation. A WEBS device recorded these signals and several novel methods of human-to-machine communication were demonstrated. To evaluate the performance of the WEBS system, recordings were validated against a high-end biopotential recording system for a number of biopotential signal types. To demonstrate an application for use by a clinician, the WEBS system was used to record 12‑lead electrocardiogram with augmented mechanical movement information

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Velocity-based training: Monitoring, implementation and effects on strength and power

Traditionally, resistance training has been prescribed using percent-based training (PBT) methods that use the loads relative to a maximal load lifted for one repetition (1RM). However, PBT does not take into account possible day-to-day fluctuations in performance that may occur from physical or psychological stressors. One approach to address this limitation is to monitor velocity changes during resistance training, based on research showing that declines in velocity are highly correlated with fatigue. Therefore, velocity-based training (VBT) methods are proposed to provide a more objective method to modify resistance training sessions based on individual differences in day-to-day performance and the rate of training adaptation. However, at the commencement of this dissertation in 2014, no previous research had examined VBT methods in comparison to PBT methods. Thus, this thesis aimed to verify the efficacy of different VBT methods using a resistance-trained population who could lift a minimum of 150% their own body mass for at least one repetition in the full-depth back squat. These parameters were chosen so that the findings of this research were applicable to strength-trained athletes who were likely to employ VBT methods in their resistance training programs. In the first of five research studies, two portable VBT devices were examined for their accuracy to assess peak velocity (PV) and mean velocity (MV) among other kinematic variables. On three separate days, ten strength-trained men performed three 1RM back squat trials that comprised loads of 20%, 40%, 60%, 80%, 90% and 100% of 1RM. Acceptable validity criteria was based on a Pearson moment correlation coefficient \u3e0.70, coefficient of variation (CV) ≤10% and Cohen d effect size (ES) r = 0.94 – 0.97, CV = 2.9 – 5.8%) and MV (r = 0.95 – 0.99, CV = 3.2 – 4.5%) across the relative load spectrum when compared to laboratory testing equipment. Thus, for the remainder of the VBT studies in this PhD thesis project, an LT was used to report the velocity data. In the second study, a novel velocity-based load monitoring method was investigated using 17 strength-trained men who performed three 1RM trials on separate days. Specifically, the reliability and validity of the load-velocity relationship to predict the back squat 1RM was calculated by entering MV at 100% 1RM into individualised linear regression equations which were derived from the load-velocity relationship of three (20%, 40%, 60% of 1RM), four (20%, 40%, 60%, 80% 1RM), or five (20%, 40%, 60%, 80%, 90% 1RM) incremental warm-up sets. The results showed that this predicted 1RM method was moderately reliable (ICC = 0.72 – 0.92, CV = 7.4 – 12.8%), and moderately valid (r = 0.78 – 0.93, CV = 5.7 – 12.2%). However, it could not be used as a VBT method to accurately modify training loads, since it significantly over-predicted the actual 1RM (SEE = 10.6 – 17.2 kg) due to the large variability of MV at 100% 1RM (ICC = 0.42, SEM = 0.05 m·s-1, CV = 22.5%). Therefore, this 1RM prediction method was no longer utilised as a method of adjusting training load for the remainder of the project. Despite its suggested importance, research had yet to investigate if velocity was stable between training sessions, so that individualised load-velocity profiles (LVP) could be created to track changes in velocity. Thus, the third study attempted to fill this research gap, where 18 strength-trained men performed three 1RM trials, which included warm-up loads pertaining to 20%, 40%, 60%, 80%, 90% and 100% 1RM, with the velocity of each repetition assessed by LT. It was found that PV, mean propulsive velocity (MPV) and MV were all reliable (ICC \u3e 0.70, CV ≤ 10%, ES \u3c 0.6) for the back squat performed at 20%, 40%, 60%, 80%, and 90% 1RM but not at 100% 1RM for MPV and MV. This meant that all three concentric velocity types could be used to develop LVPs. In addition, the smallest detectable difference was established across the relative load spectrum for PV (0.11 – 0.19 m·s-1), MPV (0.08 – 0.11 m·s-1) and MV (0.06 – 0.11 m·s-1), which then allows coaches to determine meaningful changes in velocity from their athletes between training sessions. Collectively, these results showed that LVPs could be utilised as a VBT method for monitoring sessional changes in velocity and modifying resistance-training loads according to individual differences in day-to-day performance. The fourth study compared the kinetic and kinematic data from three different VBT sessions and a PBT session in order to provide programmatic guidance to strength coaches who may choose to implement these novel methods to adjust resistance training load or volume. Fifteen strength-trained men performed four randomised resistance-training sessions 96 hours apart, which included a PBT session involving five sets of five repetitions at 80% 1RM, a LVP session (verified from Study 3) consisting of five sets of five repetitions with a load that could be adjusted to achieve a target velocity from an individualised LVP regression equation at 80% 1RM, a fixed sets 20% velocity loss threshold FSVL20 session that contained five sets at 80% of 1RM but sets were terminated once MV dropped below 20% of the maximal attainable MV from the first set or when five repetitions were completed, a variable sets 20% velocity loss threshold VSVL20 session that comprised 25 repetitions in total but participants performed as many repetitions in a set until the 20% velocity loss threshold was exceeded or 25 repetitions was completed. During the LVP and FSVL20 sessions, individuals performed repetitions with faster (p \u3c 0.05) sessional MV (ES = 0.81 – 1.05) and PV (ES = 0.98 – 1.12), avoided additional mechanical stress with less time under tension but maintained similar force and power outputs when compared to the PBT session. Therefore, the LVP and FSVL20 methods could be employed in a strength-oriented training phase to diminish fatigue-induced decreases in movement velocity that can occur in PBT. The VBT method employed in the fifth and final study was derived from the results of Study 4. Both the LVP and FSVL20 methods permitted faster repetition velocities throughout a training session compared to PBT, but it was decided that the FSVL20 method could decrease total training volume and reduce the training stimulus, which may be unwarranted. Therefore, in Study 5, the effects of the LVP-VBT approach (VBT) versus PBT on changes in strength, power and sports performance measures following six weeks of back squat training were examined. The study involved 24 strength-trained men who performed back squat training three times per week in a daily undulating format. The training protocols were matched for sets and repetitions but differed in the assigned training load. PBT group trained with relative loads varying from 59% – 85% 1RM, whereas the VBT group trained with loads that could be adjusted to achieve a target velocity from an individualised LVP that corresponded with 59% – 85% 1RM. Pre- and post-training assessments included 1RM, 30% of 1RM countermovement jump (CMJ), 20-m sprint, and 505 change of direction test (COD). Overall, the VBT group performed repetitions with faster velocities during training (p \u3c 0.05, MV = 0.76 m·s-1 vs. 0.66 m·s-1) that were perceived as less difficult (p \u3c 0.05, rating of perceived exertion = 5.1 vs. 6.0), and utilized marginally lower training loads (p \u3c 0.05, ~1.7%1RM) compared to PBT. Both VBT and PBT methods were effective for significantly enhancing 1RM (VBT: 11.3% vs. PBT: 12.5%), CMJ peak power (VBT: 7.4% vs. PBT: 6.0%), 20-m sprint (VBT: -1.9% vs. PBT: -0.9%), and COD (VBT: -5.4% vs. PBT: -3.6%). No significant differences were observed between groups for any testing assessment but likely favourable training effects were observed in 1RM for PBT group, whilst VBT group had likely favourable improvements in 5-m sprint time, and possibly favourable improvements in 10-m sprint time, and COD time. These findings suggest that both VBT and PBT methods are similarly effective; however, PBT may provide a slight 1RM strength advantage whilst VBT may be preferred by some individuals, since it permits faster training velocities, is perceived as less difficult, and is a more objective method for adjusting training load to account for individual differences in the rate of training adaptation. In conclusion, VBT (LVP approach) and PBT are similarly effective for promoting significant improvements in strength, power and sports performance tasks in strength-trained participants. However, even though the LVP-based VBT method did not provide significant increases in strength and power adaptations compared to PBT, it provided similar improvements while avoiding additional mechanical loading which may be important for the better management of training load, particularly with athletes who partake in numerous training modalities which can influence fatigue and recovery. That being said, if all repetitions are performed with maximal intended velocity but not to concentric muscular failure, a well-planned, periodised resistance training program with regular training frequency and progressive overload that accounts for bouts of recovery will provide adequate stimulus to significantly enhance strength, power and performance tasks like sprinting and changes in direction. Future training studies may look to examine the efficacy of VBT methods using multiple exercises (upper and lower body), and with different populations including women, adolescents, older adults, and potentially individuals during rehabilitation from injury so that training progress can be objectively monitored. Furthermore, future studies could look to incorporate multiple VBT methods into a training program such as the LVP method to modify resistance training load and the velocity loss thresholds method to control resistance training volume

Computer vision-based structural health monitoring and condition assessment for small to medium-span bridges

Many UK bridges are currently nearing the end of their life cycles, and therefore their maintenance and inspection routines assume a higher priority, since they become more likely to fail. Bridge maintenance routines are still predominantly based on visual inspections which are expensive, time and labour consuming and prone to human error. Structural Health Monitoring (SHM) systems, mostly utilising contact sensors have been introduced to some larger structures such as the Humber and Severn bridges to complement visual inspections. Contact sensing, however, requires access to the structure, involves working at height and is expensive. More importantly, while many larger structures have received the aid of SHM, older assets, especially small bridges with small traffic volumes still rely solely on visual inspections due to high costs associated with such SHM systems. These challenges can be circumvented by employing the fast-emerging computer-vision based structural health monitoring systems (CV-SHM), which are much more affordable, can be set up to not require working at height, nor require direct access to the structure, and which do not cause traffic disruptions. This thesis proposes an affordable and accurate CV-SHM and damage detection system to complement conventional bridge inspection routines for small to medium-size bridges. The framework comprises firstly, a computer vision (CV)-based sensing system which consist of a consumer-grade image acquisition device such as a GoPro or smartphone camera, a computer, and an image processing algorithm. This system obtains structural response without access to the structure by using image processing and feature detection techniques to analyse images of a structure captured during loading and obtain its structural response. The second part of this framework is the response analysis for damage detection and characterisation. Here, a set of data-based techniques are developed based on response information such as displacements, curvatures, inclination angles, and strains. Displacements serve as primary response obtained from the monitoring process. Others (strain, curvatures, and inclination angles) are secondary responses obtained by manipulating the primary. In this approach, response from any section of the structure can be analysed, without the requirement for further structural information, such as flexural rigidity (EI), or distance to support, unlike similar studies in literature. The condition of a structure can then be determined by comparing response measurements collected at a first inspection Others (strain, curvatures, and inclination angles) are secondary responses obtained by manipulating the primary. In this approach, response from any section of the structure can be analysed, without the requirement for further structural information, such as flexural rigidity (EI), or distance to support, unlike similar studies in literature. The condition of a structure can then be determined by comparing response measurements collected at a first inspection

The public health potential of mobile applications to increase physical activity

Background: Physical activity (PA) is an important behavioural determinant of morbidity and mortality and is a public health priority. The accessibility, convenience and wide reach of mobile applications (apps) makes these digital interventions a potential mode for delivering PA interventions at scale. At the end of 2017 there were 325,000 health apps available publicly, with “fitness” apps being the largest category of all health apps. However, most apps on the market have not been evaluated and little is known about their quality. Aim: This PhD investigated the public health potential of publicly available PA apps. Methods: The following studies were conducted: 1) a review and content analysis of the most popular PA apps on the market to assess their quality, defined as safety, likely efficacy and positive user experience; 2) a study using regression models to determine the association between popularity and quality of those apps; 3) a feasibility crossover trial assessing two apps for increasing PA; and 4) a qualitative study assessing the acceptability of the trial procedures and exploring the experiences of the two PA apps. Results: Popular apps had high usability but there were issues around their safety and likely efficacy. Popularity was not associated with likely efficacy. The feasibility trial and the qualitative study showed that such a trial would be feasible and acceptable to participants. The enablers and barriers to increasing exercise using the apps were identified. Conclusion: The discrepancy between quality and popularity represents a missed opportunity for behaviour change interventions. Hence, the public health impact of PA apps is unlikely to be achieved when market forces “prescribe” what is used by the public. The motivation to use the apps varied substantially and it is important to identify when, for whom, and in what context PA apps are most likely to facilitate behaviour change