Non-laboratory Environments Research Articles

Pre-and-post impact fall detection based on support vector machines using inertial and barometric pressure data

Objective: This paper presents a novel real-time algorithm for fall detection, which contextualizes falls by identifying activities occurring both pre- and post-impact utilizing machine learning techniques and wearable sensors. Methodology: The activities selected to contextualize fall events included standing, lying, walking, running, climbing stairs, and using the elevator. Data were collected using an inertial measurement unit and a barometric altimeter positioned on the participants’ lower backs. Thirteen healthy subjects were observed performing the activities and fall events were recorded from five healthy subjects. The proposed algorithm combines thresholding and cascade support vector machines (SVMs), whose robustness is enhanced by a verification process of the subject’s posture aimed at determining the occurrence of the fall more accurately. Results: The performance of the algorithm was evaluated in terms of the hit rate (HT) both offline and in real-time. From the activities studied, stairs climbing proved to be the most challenging to detect, with an offline HT of 85% and an online HT of 76 %. The overall offline performance was superior, with an HT of 96 %, compared to the performance achieved online, an HT of 91 %; in both cases the fall detection HT was 100 %. Conclusions: The algorithm can be used to recognize fall events occurring to any user, as it has the advantage of not needing prior adaptation due to the nonlinear nature of the SVMs. The cascade SVMs allow for using small sets of variables, leading to low computational cost and a suitable real-time implementation. These features, in addition to the posture verification process, make our algorithm suitable for activity recognition in non-laboratory environments.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Impacts of illegal trade on socio-emotional and behavioural skills in macaques.

Decades of research have illuminated the consequences of early adverse rearing experiences in laboratory macaque populations. However, limited knowledge exists about the impact of traumatic episodes in non-laboratory environments. This study delves into the repercussions of illegal trade on socio-emotional and behavioural skills in five macaque species, all victims of poaching. We categorised their past experiences into seven aspects, encompassing maternal care and interactions with conspecifics. We assessed social engagement and cooperation by analysing social behaviours and employing the Social Responsiveness Scale. Emotional resilience was evaluated by measuring anxiety levels and the occurrence of abnormal behaviours, supported by a welfare questionnaire. Additionally, the introduction of Cattell's 16PF questionnaire in macaques for the first time aimed to reveal the influence of traumatic experiences on their personality traits. Our findings emphasise the significance of early social exposure. The lack of juvenile social contact predicts reduced social behaviours and an inclination towards social avoidance in adulthood. Macaques raised by humans tend to exhibit more abnormal behaviours in social contexts, compromising their welfare. Deprivation of social exposure in infancy negatively impacts psychological stimulation and overall welfare. The duration of time spent in illegal trade correlates with heightened anxiety levels. Personality traits such as 'Calmness' and 'Unfriendliness' are influenced by rearing conditions, with macaques deprived of social interaction during their early years showing higher levels of introversion. In conclusion, the absence of social exposure during early life and hand-rearing due to illegal trade significantly shape macaques' personality traits and their social and emotional skills.

Compact and Fully Integrated LED Quantum Sensor Based on NV Centers in Diamond

Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in diamond nano or microcrystals is a promising technology for sensitive, integrated magnetic-field sensors. Currently, this technology is still cost-intensive and mainly found in research. Here we propose one of the smallest fully integrated quantum sensors to date based on nitrogen vacancy (NV) centers in diamond microcrystals. It is an extremely cost-effective device that integrates a pump light source, photodiode, microwave antenna, filtering and fluorescence detection. Thus, the sensor offers an all-electric interface without the need to adjust or connect optical components. A sensitivity of 28.32nT/Hz and a theoretical shot noise limited sensitivity of 2.87 nT/Hz is reached. Since only generally available parts were used, the sensor can be easily produced in a small series. The form factor of (6.9 × 3.9 × 15.9) mm3 combined with the integration level is the smallest fully integrated NV-based sensor proposed so far. With a power consumption of around 0.1W, this sensor becomes interesting for a wide range of stationary and handheld systems. This development paves the way for the wide usage of quantum magnetometers in non-laboratory environments and technical applications.

Low-Background CRISPR/Cas12a Sensors for Versatile Live-Cell Biosensing.

The trans-cleavage activity of CRISPR/Cas12a has been widely used in biosensing. However, many CRISPR/Cas12a-based biosensors, especially those that work in "on-off-on" mode, usually suffer from high background and thus impossible intracellular application. Herein, this problem is efficiently overcome by elaborately designing the activator strand (AS) of CRISPR/Cas12a using the "RESET" effect found by our group. The activation ability of the as-designed AS to CRISPR/Cas12a can be easily inhibited, thus assuring a low background for subsequent biosensing applications, which not only benefits the detection sensitivity improvement of CRISPR/Cas12a-based biosensors but also promotes their applications in live cells as well as makes it possible to design high-performance biosensors with greatly improved flexibility, thus achieving the analysis of a wide range of targets. As examples, by using different strategies such as strand displacement, strand cleavage, and aptamer-substrate interaction to reactivate the inhibited enzyme activity, several CRISPR/Cas12a-based biosensing systems are developed for the sensitive and specific detection of different targets, including nucleic acid (miR-21), biological small molecules (ATP), and enzymes (hOGG1), giving the detection limits of 0.96 pM, 8.6 μM, and 8.3 × 10-5 U/mL, respectively. Thanks to the low background, these biosensors are demonstrated to work well for the accurate imaging analysis of different biomolecules in live cells. Moreover, we also demonstrate that these sensing systems can be easily combined with lateral flow assay (LFA), thus holding great potential in point-of-care testing, especially in poorly equipped or nonlaboratory environments.

A combined magnetic field stabilization system for improving the stability of 40Ca+ optical clock

Future applications of portable 40Ca+ optical clocks require reliable magnetic field stabilization to improve frequency stability, which can be achieved by implementing an active and passive magnetic field noise suppression system. On the one hand, we have optimized the magnetic shielding performance of the portable optical clock by reducing its apertures and optimizing its geometry; on the other hand, we have introduced an active magnetic field noise suppression system to further suppress the magnetic field noise experienced by the ions. These efforts reduced the ambient magnetic field noise by about 10000 times, significantly reduced the linewidth of the clock transition spectrum, improved the stability of the portable 40Ca+ optical clock, and created the conditions for using portable optical clocks in non-laboratory magnetic field environments. This active magnetic field suppression scheme has the advantages of simple installation and wide applicability.

Edge-based wireless imaging system for continuous monitoring of insect pests in a remote outdoor mango orchard

Mango production is a prominent tropical fruit industry worldwide. However, outdoor mango cultivation is susceptible to crop damage caused by insect pests and harsh environmental conditions. Integrated pest management (IPM) has emerged as a proposed solution to this problem. IPM utilizes data-driven and environmentally-friendly methods to suppress insect pest populations. Nevertheless, the collection of insect pest population data remains a laborious process, necessitating automation. This paper presents an image-based monitoring system to automatically record insect pest populations and environmental conditions in mango orchards. The system comprises solar-powered sensor nodes capable of periodically acquiring and analyzing sticky paper trap images. A modular deep learning-based algorithm was developed to detect and classify insect pests into seven classes, including major insect pests of mango such as thrips, mango leafhopper, and oriental fruit fly, with an average classification F1-score of 0.96. Unlike other insect counting algorithms, the algorithm reliably classifies insect pests according to different taxonomic levels even in non-laboratory environments. The monitoring system was tested and deployed in a remote mango orchard for over two years. The collected spatiotemporal information was analyzed to demonstrate the benefits of using the proposed system and recommend new IPM strategies. Temporal data analysis revealed a significant decrease in the count of selected insect pests after using the system, enabling identification of insect hotspots through statistical methods. This work presents a breakthrough in hardware and software solutions for developing smarter insect pest monitoring systems, leading to better IPM strategies.

Connective Adaptive Resistance Exercise (CARE) Machines for Accentuated Eccentric and Eccentric-Only Exercise: Introduction to an Emerging Concept

Eccentric resistance exercise emphasizes active muscle lengthening against resistance. In the past 15 years, researchers and practitioners have expressed considerable interest in accentuated eccentric (i.e., eccentric overload) and eccentric-only resistance exercise as strategies for enhancing performance and preventing and rehabilitating injuries. However, delivery of eccentric resistance exercise has been challenging because of equipment limitations. Previously, we briefly introduced the concept of connected adaptive resistance exercise (CARE)—the integration of software and hardware to provide a resistance that adjusts in real time and in response to the individual’s volitional force within and between repetitions. The aim of the current paper is to expand this discussion and explain the potential for CARE technology to improve the delivery of eccentric resistance exercise in various settings. First, we overview existing resistance exercise equipment and highlight its limitations for delivering eccentric resistance exercise. Second, we describe CARE and explain how it can accomplish accentuated eccentric and eccentric-only resistance exercise in a new way. We supplement this discussion with preliminary data collected with CARE technology in laboratory and non-laboratory environments. Finally, we discuss the potential for CARE technology to deliver eccentric resistance exercise for various purposes, e.g., research studies, rehabilitation programs, and home-based or telehealth interventions. Overall, CARE technology appears to permit completion of eccentric resistance exercise feasibly in both laboratory and non-laboratory environments and thus has implications for researchers and practitioners in the fields of sports medicine, physiotherapy, exercise physiology, and strength and conditioning. Nevertheless, formal investigations into the impact of CARE technology on participation in eccentric resistance exercise and clinical outcomes are still required.

All-fiber-device-coupled compact, transportable ultra-stable laser.

In response to the demand for operation in non-laboratory environments, there has been a trend toward the development of compact, transportable ultra-stable lasers. This paper reports on this sort of laser system assembled in a cabinet. The whole optical part utilizes fiber-coupled devices to simplify the integration. In addition, spatial beam collimation and alignment into the high-finesse cavity are realized by a five-axis positioner and a focus-adjustable fiber collimator, which significantly relax the alignment and adjustment. A theoretical analysis is performed on how the collimator adjusts the beam profile and coupling efficiency. The support structure of the system is specially designed as well so that it features robustness and transportation without performance degradation. The observed linewidth is 1.4Hz within a duration of 1s. After subtracting the linear drift of 70 mHz/s, the fractional frequency instability is better than 4 × 10-15, for the averaging time ranging from 1 to 100s, which is close to the thermal noise limit of the high-finesse cavity.

Rapid, field-deployable method for determination of seafood decomposition by compact mass spectrometry

Seeking alternatives to sensory which can be performed in non-laboratory field environments has been a recent emphasis in regulatory assessment of seafood decomposition. To this end, an international sampling effort produced samples of 23 different seafood products, which were subjected to controlled decomposition of varying levels on ice. This produced samples of each product for a wide range of decomposition states, and these were scored by a sensory expert on a standard 1-100 scale. Samples were then analyzed by a novel technique involving headspace analysis and compact mass spectrometry. The mass spectrometry data was used to create computer models, guided by the sensory data, to generate a calculated score analogous to a sensory score. Models based on a training set of samples were then used to calculate scores for a test set, and accuracy was assessed by comparing these calculated scores to original sensory data. Calculated scores agreed with sensory findings with respect to overall decomposition state for 96.3% of samples (n=547), with seven false positive (1.3%) and 13 false negative (2.4%) findings. Reproducibility was also assessed via triplicate analysis on separate days for low, middle, and high decomposition states. All sample ranges for these replicates were within 20 points, with 89% (n=66) within 15 points, 73% within 10, and 35% within five. Given the performance of the method, along with the small form factor and manageable operation requirements of the instrument as well as simplicity of sample preparation, this appears to offer a potentially useful technique for field testing of seafood decomposition.

Towards a Deep Learning-based Online Quality Prediction System for Welding Processes

The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly benefit from data-driven solutions is gas metal arc welding (GMAW). The welding process is characterized by complex cause-effect relationships between material properties, process conditions and weld quality. In non-laboratory environments with frequently changing process parameters, accurate determination of weld quality by destructive testing is economically unfeasible. Deep learning offers the potential to identify the relationships in available process data and predict the weld quality from process observations. In this paper, we present a concept for a deep learning based predictive quality system in GMAW. At its core, the concept involves a pipeline consisting of four major phases: collection and management of multi-sensor data (e.g. current and voltage), real-time processing and feature engineering of the time series data by means of autoencoders, training and deployment of suitable recurrent deep learning models for quality predictions, and model evolutions under changing process conditions using continual learning. The concept provides the foundation for future research activities in which we will realize an online predictive quality system for running production.

Engineering Controls and Technologies to Enhance Safety in the COVID-19 Pandemic Landscape: Lessons for Laboratories and Non-Laboratories.

The global SARS-CoV-2 pandemic ushered in a new way of life in a short time, with many lasting impacts that have yet to be fully realized. This pandemic threat landscape resulted in massive efforts to increase safety, minimize person-to-person transmission, and rethink how society approaches personal and collective health issues. The buildings and environments in which we live, work, and learn now became environments that pose new risks. As a result, many institutions began asking what improvements could be made to those environments to reduce the spread of infection of SARS-CoV-2 and other infectious diseases. The authors conducted a review of past projects and emerging technologies to evaluate which applications in containment laboratories could represent an example of how engineering controls can improve safety by protecting the workers inside the laboratories as well as the public interfacing the laboratories. Engineering controls, technology, and safety systems are hallmarks of modern containment laboratories that may provide some context into extrapolating these elements into non-laboratory environments, providing there is coordination with a risk assessment methodology. In this study, the authors explore new technologies proposed for controlling SARS-CoV-2 in heating, ventilation, and air conditioning systems, and potential impacts to the operations and maintenance of those systems.

Calibration of an RF/Microwave Phase Noise Meter with a Photonic Delay Line

The main advantage of phase noise meters with photonic (fiber) delay lines is that they do not require high-performance, low-noise reference oscillators. On the other hand, some additional calibrations are required, which are the subject of this paper. First, the quadrature must be maintained on the mixer by precise adjustment of the phase and/or delay. Next, since the response of the mixer is proportional to the square of the input test signal, a precise amplitude calibration is required. Finally, the frequency response of the FFT spectrum analyzer and its corresponding anti-aliasing low-pass filter needs to be known precisely. In this paper, to the best of our knowledge, we present innovative solutions for all three calibrations. All three calibrations were built in and tested in our phase noise meter. The result is a simple and robust phase noise meter suitable for non-laboratory environments.

The impact of sampling frequency on ground reaction force variables

New portable and low-cost technologies for assessing limb loading may be useful in non-laboratory environments, but have relatively low sampling frequencies. The lowest recommended sampling frequency for impact kinetics has not been investigated. The purpose of this study was to determine the effect of sampling frequency on metrics of impact kinetics during landing, walking, and running. This was a retrospective analysis of bilateral drop vertical jumps, unilateral drop landings, treadmill running, and flat, inclined, and declined treadmill walking. Landing data were collected at 1920 Hz while walking and running data were collected at 1440 Hz. Impact kinetics were computed at the highest possible sampling frequency, and then data were continuously down-sampled to determine the impact on the following computed metrics: peak impact force, average LR, and impulse. The minimum sampling frequency to compute each outcome with 90%, 95%, and 99.5% accuracy when compared to the original sampling frequency were determined. To achieve 90% of the true value of impact force, a sampling frequency of 180 Hz was needed for running, 62 Hz for bilateral landing, and 48 Hz for remaining tasks. For average LR, a sampling frequency of 1440 Hz was need for running, 63 Hz for inclined walking, 192 Hz for bilateral landing, and 48 Hz for the remaining tasks. For impulse, 48 Hz was required for all tasks. The results of this study provide future researchers with a guide for selecting the sampling frequency required to accurately assess impact kinetics during walking, landing, or running.

Validation of Soft Multipin Dry EEG Electrodes.

Current developments towards multipin, dry electrodes in electroencephalography (EEG) are promising for applications in non-laboratory environments. Dry electrodes do not require the application of conductive gel, which mostly confines the use of gel EEG systems to the laboratory environment. The aim of this study is to validate soft, multipin, dry EEG electrodes by comparing their performance to conventional gel EEG electrodes. Fifteen healthy volunteers performed three tasks, with a 32-channel gel EEG system and a 32-channel dry EEG system: the 40 Hz Auditory Steady-State Response (ASSR), the checkerboard paradigm, and an eyes open/closed task. Within-subject analyses were performed to compare the signal quality in the time, frequency, and spatial domains. The results showed strong similarities between the two systems in the time and frequency domains, with strong correlations of the visual (ρ = 0.89) and auditory evoked potential (ρ = 0.81), and moderate to strong correlations for the alpha band during eye closure (ρ = 0.81–0.86) and the 40 Hz-ASSR power (ρ = 0.66–0.72), respectively. However, delta and theta band power was significantly increased, and the signal-to-noise ratio was significantly decreased for the dry EEG system. Topographical distributions were comparable for both systems. Moreover, the application time of the dry EEG system was significantly shorter (8 min). It can be concluded that the soft, multipin dry EEG system can be used in brain activity research with similar accuracy as conventional gel electrodes.

SpeechCollectR: An R package for web-based speech data collection

To accommodate web-based speech production and perception data collection during the COVID-19 pandemic, we developed the R package, SpeechCollectR (https://github.com/abbey-thomas/speechcollectr), designed for speech scientists familiar with R [R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021]. SpeechCollectR employs existing R functions for acoustic analysis, allowing researchers to collect, visualize, and analyze speech production and perception data with a single program. SpeechCollectR lets researchers construct interfaces that embed experimental tasks in games to sustain participant attention amid distractions in non-laboratory environments. The application for production experiments saves uncompressed audio data as WAV files, one recording per trial. Amplitude envelope features are used to isolate a speech token in each recording. This token is compared to the remainder of the recording to assess signal-to-noise ratio. Performing this analysis after each trial, we can supply the participant with feedback to improve recording quality. We tested the efficacy of using SpeechCollectR, collecting 1344 recordings of three types of affective prosody from 32 participants. 83% of these recordings were usable for analysis of prosodic variables. We present a bootstrap analysis demonstrating reliability of affective prosodic features despite variations in recording equipment and environment.

Camera motion-induced systematic errors in stereo-DIC and speckle-based compensation method

Stereo-digital image correlation (stereo-DIC) is now a standard technique for determination of the mechanical properties of materials and structures. In stereo-DIC, cameras are assumed to be motionless after camera calibration, so the three-dimensional (3D) reconstruction can be implemented using pre-calibrated parameters. However, this assumption is not true in some situations, such as drop test, seismic shaking table test and non-laboratory environment. Due to the presence of ground shaking or wind blowing, it's almost impossible to avoid camera motion in these experiments even if mechanical fixing is adopted. Camera motion during the experiment can undoubtedly introduce significant errors on measured results. Generally, camera motion can be divided into pair motion and relative motion. The influence of these two kinds of motion on stereo-DIC measurement is different and worth of separate study. Keep this in mind, in this paper, the influence of camera motion on stereo-DIC is investigated and the effects of pair motion and relative motion are examined separately using precisely controllable simulated experiments. Specifically, by integrating into the stereo matching, the speckle-based compensation method for relative motion is proposed and the accuracy of the compensation methods is analyzed in detail. The reduction of camera motion-induced systematic errors will be helpful for the further applications of stereo-DIC in non-laboratory environments and engineering fields.

CRISPR-cas12a mediated SERS lateral flow assay for amplification-free detection of double-stranded DNA and single-base mutation

Lateral flow assay (LFA) is user-friendly diagnostic tools but suffering limitations in poorer sensitivities and specificities especially for double-stranded DNA and single-base mutation quantification. Here, to improve the sensitivity and specificity for the LFA based nucleic acid detection, CRISPR-Cas12a mediated Surface enhanced Raman scattering (SERS) LFA was developed. By combination of ultra-sensitive SERS tags and target-specific signal amplification ability of CRISPR-Cas12a, HIV-1 dsDNA can be directly quantified with a LOD of 0.3 fM without any pre-amplification steps, which is almost 4 orders of magnitude lower than that of traditional colorimetric LFA methods. The whole detection process can be finished less than 1 h. Moreover, based on the target specificity of Cas12a, HIV-1 single-based drug resistance mutation (M184V) can be recognized as low as 0.01%. The HIV-1 dsDNA can also be successfully detected in serum samples with good comparable of that in buffer setting. Therefore, the simple and inexpensive paper-based CRISPR-SERS strip has great potential for point-of-care testing (POCT) of nucleic acid targets especially in resource-poor or non-laboratory environments.

A novel model to quantify balance alterations in older adults based on the center of pressure (CoP) measurements with a cross-sectional study.

BackgroundThe timely detection of fall risk or balance impairment in older adults is transcendental because, based on a reliable diagnosis, clinical actions can be taken to prevent accidents. This study presents a statistical model to estimate the fall risk from the center of pressure (CoP) data.MethodsThis study is a cross-sectional analysis from a cohort of community-dwelling older adults aged 60 and over living in Mexico City. CoP balance assessments were conducted in 414 older adults (72.2% females) with a mean age of 70.23 ± 6.68, using a modified and previously validated Wii Balance Board (MWBB) platform. From this information, 78 CoP indexes were calculated and analyzed. Multiple logistic regression models were fitted in order to estimate the relationship between balance alteration and the CoP indexes and other covariables.ResultsThe CoP velocity index in the Antero-Posterior direction with open eyes (MVELAPOE) had the best value of area under the curve (AUC) to identify a balance alteration (0.714), and in the adjusted model, AUC was increased to 0.827. Older adults with their mean velocity higher than 14.24 mm/s had more risk of presenting a balance alteration than those below this value (OR (Odd Ratio) = 2.94, p<0.001, 95% C.I.(Confidence Interval) 1.68–5.15). Individuals with increased age and BMI were more likely to present a balance alteration (OR 1.17, p<0.001, 95% C.I. 1.12–1.23; OR 1.17, p<0.001, 95% C.I. 1.10–1.25). Contrary to what is reported in the literature, sex was not associated with presenting a balance alteration (p = 0.441, 95% C.I. 0.70–2.27).SignificanceThe proposed model had a discriminatory capacity higher than those estimated by similar means and resources to this research and was implemented in an embedded standalone system which is low-cost, portable, and easy-to-use, ideal for non-laboratory environments. The authors recommend using this technology to support and complement the clinical tools to attend to the serious public health problem represented by falls in older adults.

Bacterial cell gene expression

Bacterial Genomics Single-cell genomics in bacteria has lagged relative to in eukaryotes because of their tough bacterial cell walls, low messenger RNA content, and lack of many posttranscriptional modifications. To tackle this challenge, Kuchina et al. developed microbial split-pool ligation transcriptomics, or microSPLiT, a single-cell sequencing method for both Gram-negative and Gram-positive bacteria. Sequencing both Escherichia coli and Bacillus subtilis showed differences in the heat shock response. Examining B. subtilis transcriptional patterns revealed that a small fraction of cells grown in laboratory medium express a myo-inositol catabolism pathway, which the cell could use in nonlaboratory environments, thus highlighting how microSPLiT can identify rare cellular states. Science , this issue p. [eaba5257][1] [1]: /lookup/doi/10.1126/science.aba5257