Grants and Contributions

Trualta is developing a proprietary analytics framework to help our clients track outcomes for caregivers. The new product suite will include updated reporting capabilities, visual dashboards, as well as insights to help our clients support the families of their patients and members.

Erthos Inc. has developed a proof of concept (POC) to develop and implement a biobased, compostable solution that leverages biobased additives found both in nature and developed internally, to optimize, tune and strengthen their plant-powered compostable biocomposite resin formulations, and to achieve the following technological advancements:

1) Viable injection moulding of the compostable resins;
2) Viable film cast extrusion of the compostable biocomposite resins;
3) Viable biocomposite resins for production in commercial-scale extruders;
4) Development of internal formulae to understand material end of life behaviour

The proposed work plan consists of five (5) main activities as described below.

Falls by older adults can result in lasting and critical consequences,
including, injury, long-term disability, and even death. Screening is the first
step in identifying fall risk and preventing future falls by predicting
individuals at higher risk. At present, a history of falls and reported
abnormalities of gait are found to be the best predictors of future falls.
Novel technologies, such as sensors, can be an effective, efficient and
economical way to assess fall risk. However, there is a lack of evidence in
the use of analyzing gait through sensors to predict falls both in-home and
healthcare facilities.
UBC will collaborate with NRC to design, develop and trial a smart sensor
based solution, and provide clinical, caregiver and patient insights,
acceptability and usability testing and validation. This close clinical?technical user-centric collaborative approach will innovatively tackle a
growing need to reduce the incidence and impact of falls by older adults.

Covid-19 - Feasibility of a RAMP Multiplex Assay Format

Though green hydrogen production has the potential to reduce 37% of energy?related emissions if scaled, the process faces adoption challenges due to high costs, compromised system reliability, and loss in performance/efficiencies. Owing to the complexity of the electrolyser system, off-design operating conditions lead to performance degradation mechanisms that could not historically be monitored or managed at the industrial stack scale. Pulsenic’s Propriety Electrochemical Impedance Spectroscopy (EIS) device will enable monitoring and differentiating the degradation losses during a high current electrolysis process, which in return could provide information and data about the behaviour of the components during on/off-design operating condition. The objective of this project is to use this stack level data enabled by Pulsenic’s device to develop a model of a renewably powered electrolysis plant that correlates operating and stack design conditions with the electrolyser efficiencies. This model will map the relationship between operating/design conditions and electrolyser degradations to inform optimization choices for improved reliability and costs. Enabled by the collaborators(NRC, UQTR and uVic) technical expertise in material and electrochemical degradation characterizations and modeling innovations, this Project will produce a novel solution for managing the performance and economics of electrolysis plant operations.

Though green hydrogen production has the potential to reduce 37% of energy?related emissions if scaled, the process faces adoption challenges due to high costs, compromised system reliability, and loss in performance/efficiencies. Owing to the complexity of the electrolyser system, off-design operating conditions lead to performance degradation mechanisms that could not historically be monitored or managed at the industrial stack scale. Pulsenic’s Propriety Electrochemical Impedance Spectroscopy (EIS) device will enable monitoring and differentiating the degradation losses during a high current electrolysis process, which in return could provide information and data about the behaviour of the components during on/off-design operating condition. The objective of this project is to use this stack level data enabled by Pulsenic’s device to develop a model of a renewably powered electrolysis plant that correlates operating and stack design conditions with the electrolyser efficiencies. This model will map the relationship between operating/design conditions and electrolyser degradations to inform optimization choices for improved reliability and costs. Enabled by the collaborators(NRC, UQTR and uVic) technical expertise in material and electrochemical degradation characterizations and modeling innovations, this Project will produce a novel solution for managing the performance and economics of electrolysis plant operations.

Trax Electronics has developed, and is actively marketing, an autonomous robotic drill bit scanning and analysis system. As part of that system, proprietary 3D models are constructed to use in the analyses. The goal of this project is to enhance the capabilities of the existing system, so that it could be used in a broad range of metrology applications which would include, but not be limited to:

1. QA/QC for manufacturing applications
2. Scanning of parts to allow on-demand 3D printing to
   i) reduce supply chain issues
   ii) reduce inventory carrying costs
   iii) reduce transportation requirements

The project aims to develop and validate an in-home rehabilitation system which can guide the elderly person through a set of optimized and individualized exercises after a total joint replacement surgery, as well as to monitor for potential serious postoperative complications, such as infections and deep vein thrombosis. To ensure ease-of-use and user?friendly installation, the Project will investigate remote (not attached to the human) and cost-effective sensors technologies, such as 2D and 3D cameras, as well as thermographic cameras for this purpose. Furthermore, algorithms will be developed to detect robustly whole body motions, as well as small joint motions of the elderly user. These information can be used to provide feedback to the user in real-time to ensure the exercises are performed correctly and to the full potential. Furthermore, the newly developed methods will be tested in a preliminary clinical study and compared to state-of-the-art motions tracking methods to ensure practicality, robustness, and accuracy.

The project aims to develop and validate an in-home rehabilitation system which can guide the elderly person through a set of optimized and individualized exercises after a total joint replacement surgery, as well as to monitor for potential serious postoperative complications, such as infections and deep vein thrombosis. To ensure ease-of-use and user?friendly installation, the Project will investigate remote (not attached to the human) and cost-effective sensors technologies, such as 2D and 3D cameras, as well as thermographic cameras for this purpose. Furthermore, algorithms will be developed to detect robustly whole body motions, as well as small joint motions of the elderly user. These information can be used to provide feedback to the user in real-time to ensure the exercises are performed correctly and to the full potential. Furthermore, the newly developed methods will be tested in a preliminary clinical study and compared to state-of-the-art motions tracking methods to ensure practicality, robustness, and accuracy.

Driving cessation for many aging people is associated with a poorer quality of life brought about by the loss of mobility and independence, which ultimately can lead to physical and mental health problems such as depression, social isolation, diminished independence, etc. Connected and automated vehicle (CAV) technologies can provide feasible solutions to help ameliorate this problem. Although fully autonomous driving remains the goal of all involved R&D efforts, advanced driving assistance systems (ADASs) are already deployed at a mass scale that provide enhanced safety and warning systems and even enable partial automated driving. Recent year production vehicles are equipped with many ADAS features such as collision imminent braking systems, blind spot warning, automated lane centering, adaptive cruise control, automated parking, etc. All these features can help delay driving cessation for the aging population by compensating for the decline in cognitive capacity required for safe driving. However, current production ADAS systems do not consider driver behavior and intention. Typical implementations of ADAS systems are reactive in nature in that the safety of driving actions (acceleration and steering) are evaluated against the environment (presence of traffic ahead or in blind spots on adjacent lanes) after the fact. The proposed research aims to build an artificial intelligence (AI) based driver intentionality predictor that will augment and enhance typical ADAS systems to transform their nature from reactive to proactive. Since driver gaze directly correlates with driver intentionality and precedes driving action, this project will utilize a driver gaze monitoring system to develop an AI-based tool to predict driver intentionality and action. Such predictions can be used to provide warning or even enable automated vehicle actions, such as braking, speed reduction, collision avoidance. This will significantly improve driving safety and increase availability of mobility for aging people in the following ways: • The time horizon of conventional ADAS systems will be broadened to better include drivers with sub-optimal cognitive function. • Aging population can postpone driving cessation until much later stages in their lives without compromising their independence and the quality of life.