Wearable Technology with Environmental Sensors
Along with the emergency response equipment first responders bring to a rescue situation, there are emerging technologies that can equip them for safety in a number of ways. Wearable tech can inform supervisors if team members are experiencing any spikes in heart rate or blood pressure, as well as other biometric data, while environmental sensors can determine if any toxins or dangerous chemicals are present in the surrounding environment. Measuring blood oxygen levels via pulse oximetry sensors can tell firefighters when they’ve been overexposed to smoke-filled air, and body positioning sensors like the kind used in some step counters and other fitness trackers can sound the alarm when a first responder is lying prone or in any awkward position that might indicate potential distress. Something as simple as monitoring body temperature can let firefighters know when to pull back from the front lines and rehydrate.
Environmental sensors capable of measuring the presence of airborne pollutants or particulate matter are commonly implemented in industrial manufacturing and processing facilities for employee safety. First responders can utilize similar technology in a more mobile application to provide them with important safety information about environments they’re encountering with limited prior knowledge.
Concentrations of potentially poisonous and invisible gases in the air, like carbon monoxide or dioxide or volatile organic compounds, can be detected through chromatography and light refraction. Particulate matter created by combustion, like the kind made by forest fires, can also be detected and measured through light reflection. Larger pieces of particulate reflect more light than smaller ones and pose a greater health risk, so measuring the size of particulate fragments as precisely as possible is essential. Environmental data collected from scenes of disasters has value for medical personnel as well. Having prior knowledge of the types of airborne toxins or pollutants victims and evacuees have been exposed to before they’ve even been examined can help develop treatment plans more quickly.
Real-time Data Collection
Mobile sensors collecting real-time data on first responders’ persons feed the information into an intelligent processing layer and then display data on a “dashboard” of sorts, presenting a digital readout of the various vital signs and environmental factors being monitored. The dashboard can be monitored remotely by first responders on the scene or supervisors offsite to ensure that any responders in distress can be helped as quickly as possible. Real-time data on the surrounding air quality can tell firefighters precisely when they have to employ oxygen tanks in the field in order to breathe safely or when toxic fumes from a chemical spill have become too dangerous to be exposed to without a special breathing apparatus. Vital sign monitoring lets supervisors know when individual firefighters on the front lines of a blaze need a break or medical intervention, similar to the technology being implemented in sports to monitor athletes’ body temperature and blood oxygen levels.
Data collected in real time can also be saved and fed into algorithms that recognize patterns and make predictions to help optimize future emergency response plans. Knowing that personnel can only safely fight fires burning at certain temperatures from specific distances helps spare future hospitalizations, or worse, heatstroke or smoke inhalation. Furthermore, knowing how first responders’ bodies have reacted to the presence of certain gases or volatile organic compounds in the environment can help design emergency treatment options if first responders or victims are exposed in the future and require immediate medical attention in the field. In large-scale personnel deployments, like forest fires or natural disaster relief, historical data on employee health and wellness can help supervisors determine the optimal length and frequency of shifts to maximize overall efficiency and help responders get the appropriate amount of sleep and nutrition.
Conclusion
Working in potentially hazardous environments is something asked of first responders every day, so monitoring their vital signs as well as key environmental factors is critical to ensuring safety. Using the data collected from first responders on the front lines to optimize future emergency response plans may also save lives and ensure first responders live longer, healthier lives post-retirement. The short- and long-term benefits of wearable technology and environmental sensors are so self-evident that you may see firefighters, EMTs, and even police officers wearing bio- and environmental-metric sensors on a daily basis in the near future.
I suspect most know that AKIDA technology is already proven in this area for newer investors the following two papers cover its applicability to these types of applications.
My opinion only DYOR
Fact Finder
“5. Conclusions
In this paper, we presented the development of an SNN-based solution for real-time classification of electronic olfactory data. The SNN was implemented using Brainchip’s Akida Development Environment, which provides an emulation of the Akida NSoC’s functionalities on a Python-based software platform. The highlights of this implementation included the development of a novel AER-based encoder for olfaction data, implementation of unsupervised STDP for training the SNN, highly accurate real-time classification results, and preliminary results that lay a foundation for applying the Akida SNN for reliable early classification results.
One of the most significant contributions of this study is the development of AERO, an AER-based data-to-spike encoder for olfaction data. The operating principle of AERO is based on discretizing the sensor responses and encoding their activation levels along with sensor ID and temporal data. For this implementation, normalized relative resistance features were extracted from sensor responses and provided as an input to the AERO encoder. Parameters, such as frequency of event-generation, the number of time points for encoding, and discretization levels, can be configured based on the input data and processing requirements. The development of AERO has opened several avenues for future research, such as encoding multi-dimensional data using different features and interfacing of electronic nose systems with AER-based neuromorphic hardware for processing.
The SNN was tested using the benchmark dataset [
20] under four different scenarios of increasing complexity. In general, under each scenario, the classification performance of the SNN was between 90% and 100%, and the processing latency was between 2.5 and 3 s, which includes data-to-spike encoding, learning, classification, and other software-based latencies introduced due to looping and conditional statements. This processing latency would, of course, be dramatically reduced once the classifier is implemented on the Akida NSoC hardware without the overhead of software emulation. Taken together, these classification results show that the SNN-based classifier can deliver highly accurate results with minimal processing latency. Moreover, the ability to transfer the SNN implementation to the Akida NSoC can be leveraged to develop low-power electronic nose systems with minimal computational cost and memory requirements. Intrinsically, in most cases, neuromorphic approaches have proven to outperform traditional processing methods that suffer from limited accuracy, high computational and power requirements, and substantial latency to provide classification results [
32]. When evaluated against other neuromorphic and traditional approaches based on the same dataset [
21,
24,
33,
34], the results revealed that the SNN classifier developed in this study achieved comparable and, in most cases, better classification performance with minimal computation requirements and latency for both learning and processing. More importantly, the Akida SNN was able to identify patterns from a highly multi-dimensional dataset and classify the dataset based on the four chemical groups of the compounds.
Future research based on these results will focus on the development of a robust SNN-based classifier on the Akida NSoC and its implementation in a real-world application. The efficacy of AERO when combined with rate coding methods, such as [
35] and rank-order encoding [
2,
31,
36], will also be investigated. This implementation also lays the foundation for the application of both the AERO encoder and an SNN to study neuromorphic gustation and the fusion of olfaction and gustation for the development of a comprehensive analytical tool for chemical sensing”
In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent ...
www.ncbi.nlm.nih.gov
“4. Conclusions
This study presents the implementation of a neuromorphic approach towards the encoding and classification of electronic nose data. The proposed approach was used to identify eight classes of malts and has potential as an application for quality control in the brewing industry. Experiments were conducted using a commercial e-nose system to record a dataset consisting of time-varying information of sensor responses when exposed to different malts under semi-laboratory conditions. The classifier proposed in this study utilized the combination of the Akida SNN and the AERO encoder, a neuromorphic approach that has previously delivered highly accurate results on a benchmark machine olfaction dataset [
12]. The proposed method successfully classified the dataset with an accuracy of 97.08% and a maximum processing latency of 0.4 ms per inference when deployed on the Akida neuromorphic hardware. A secondary dataset that was used to validate the classifier model in an ‘inference-only’ mode was classified with an accuracy of 91.66%. These results could potentially be further improved by refinements to pre-processing that can enhance informative independent components for malt classes that are misclassified.
Based on these results, we can conclude that the classifier model implemented using Akida SNN in conjunction with the AERO encoder provides a promising platform for odor recognition systems. An application targeted towards the identification of malts based on their aroma profile, generally considered a nontrivial classification task using traditional machine learning algorithms, was successfully demonstrated in this work with a classification accuracy greater than 90% under different scenarios. The developed model can be deployed on the Akida NsoC, thus enabling the integration of a bio-inspired classifier model within a commercial e-nose system. A comparative analysis of the proposed approach with statistical machine learning classifiers shows that the SNN-based classifier outperforms the statistical algorithms by a significant margin for both accuracy and processing latency. A performance-based comparison of the neuromorphic model proposed in this work with other neuromorphic olfactory approaches, such as [
13,
14,
26,
27,
69,
70], could not be established as their inherent structures, including spike encoding schemes, neuron models, SNN architectures, and implementation of learning algorithms, vary vastly. The proposed methodology, however, does not require a graphic processing unit (GPU)-based model simulation, unlike in [
13], or a complex bio-realistic model, as used in [
14]. Furthermore, the SNN-based classifier can be entirely mapped on a single neural processing unit core, as opposed to multiple cores used in [
14], leading to a low-power and low-latency implementation.
The application of such real-time and highly accurate e-nose systems can be extended to fields such as food technology, the brewing and wine industries, and biosecurity. Future research in this domain will focus on encoding parameters such as rank-order code within the AERO events to analyze its impact on classification performance“
Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate ...
www.ncbi.nlm.nih.gov
ML stuff, literally. Researching ultra low-power neural networks for the edge, achieving 100x to 1000x times savings in model size, memory, and compute. I mainly developed the 
temporal event network (TENN) network for the second-generation Akida hardware, achieving state-of-the-art results in multiple tasks such as: object detection, motion planning, raw ASR, raw denoising, 
