Canada

People from around the world experience mobility impairments, leading to significant impacts on independence and quality of life. While conventional mobility assistive devices such as wheelchairs, walkers, and prosthetics offer essential support, they often lack adaptability and user responsiveness. Artificial intelligence redefines mobility devices by advancing autonomy, personalization, and user experience. This scoping review maps the current landscape of AI-powered mobility aids for adults with physical impairments. Using the Arksey and O’Malley framework and PRISMA-ScR guidelines, we systematically reviewed 39 peer-reviewed studies, which were identified through specific search strategies developed for each database. Screening and data extraction were conducted in pairs using a predefined criterion. The included studies spanned 17 countries and focused primarily on smart wheelchairs (n=22), followed by walkers (n=8), prosthetics (n=3), rollators (n=2), and individual studies on canes, wheelchairs/scooters, lower-limb braces, and body-weight support systems (n=1 each). AI models were then categorized by learning type. AI-powered mobility devices offer individual benefits like enhanced navigation, motivation, and emotional well-being, but users face challenges such as device complexity, usability issues, and fatigue. At the administrative level, these technologies reduce caregiver workload and improve efficiency, yet are hindered by insufficient training and limited user input, while policy-level barriers include inadequate funding and lack of regulatory support. These findings affirm the potential of AI to enhance mobility and autonomy and promote the urgent need for inclusive, real-world research, standardized evaluation, and equitable access across diverse populations.

Indicium 2025

AI-powered Mobility Devices for Adults With Impairments: A Scoping Review

artificial intelligence; machine learning; mobility assistive devices; human-machine interaction; smart wheelchairs and walkers

technical paper

This annual scholarly program is designed to provide undergraduate students with a structured introduction to laboratory research within their home institution. Participants are matched with principal investigators and mentored by graduate students, enabling them to engage directly in ongoing research projects and develop foundational skills in scientific inquiry.

This annual scholarly program is designed to provide undergraduate students with a structured introduction to laboratory research within their home institution.
This year’s cohort focused on projects in the field of longevity science, exploring diverse topics such as bioinformatics, molecular biology, and cognitive science.

As populations age globally, older adults are increasingly affected by chronic physical and mental health challenges, including frailty, cognitive impairment, depression, and social isolation. Artificial intelligence (AI) tools such as chatbots, wearable monitors, predictive models, and socially assistive robots have been proposed as scalable solutions to support aging populations. This literature review systematically examined 28 peer-reviewed studies to evaluate the efficacy and usability of current AI interventions designed to enhance health and well-being in adults aged 65 and older. Methods included supervised machine learning for diagnosis and prediction, large language models for health communication, and co-designed systems for home-based care and emotional support. Results suggest that AI applications can improve physical activity, emotional regulation, and early detection of cognitive decline and depression, with user feedback indicating positive perceptions of usability and engagement. However, most studies had small, homogeneous samples, lacked long-term follow-up, and were conducted in simulated or non-clinical settings. Ethical and implementation challenges were common, including data privacy concerns, unclear algorithmic decision-making, limited digital literacy, and underrepresentation of diverse older adult populations. Despite these limitations, the evidence indicates that AI holds promise in augmenting and streamlining older adult care when tools are transparent, inclusive, and tailored to user needs. Broader adoption will require rigorous evaluation in real-world environments, equitable design practices, and sustained attention to ethical risks.

Artificial Intelligence for Care for Older Adults with Chronic Health Conditions

Loneliness is increasingly recognized as a non-motor symptom in Parkinson’s disease (PD), impacting mood, cognitive decline, and even mortality. Individuals with PD are vulnerable due to physical limitations, communication challenges, and stigma. This review synthesizes peer-reviewed literature on the intersection of loneliness, social isolation, and PD. Using Covidence, we screened 232 abstracts and subsequently reviewed 102 full-text articles. Twenty contributed contextual or descriptive insights, while only four studies provided structured, analyzable data using validated measurement tools or reporting statistical associations with clinical outcomes. Most studies were observational, published between 2020 and 2024, in English. Where reported, loneliness prevalence in PD populations ranged from 24% to over 30%, and was linked to poor quality of life, depression, and a greater disease burden. One prospective study identified a significant association between loneliness and incident PD (HR=1.25, fully adjusted), while others connected loneliness to reduced autonomy and poorer functional outcomes. However, intervention studies aimed at alleviating loneliness were notably rare and typically descriptive, with no high-quality trials identified. A concern was the limited use of formal risk bias tools, with only 17% of studies employing one. Furthermore, conflict of interest disclosures were present in a mere 13% of the articles included. While 60% of studies did include reviewer's comments on bias, these lacked standardization. This review underscores a growing but methodologically inconsistent body of literature. Improved standardization is crucial to develop evidence-based targeted interventions to address loneliness in individuals with PD.

Bridges in Parkinson’s Disease: Connecting Lives, Ending Loneliness

As the global economy expands, its environmental consequences intensify. Atmospheric CO2 now exceeds 420 ppm and continues rising. Greenhouse gases use CO2 enrichment to boost crop yields, but most rely on fossil fuel-derived CO2. This study models a Direct Air Capture (DAC) system using Zeolite 13X to recycle CO2 for greenhouse enrichment. Regeneration via solar, electricity, and natural gas was compared to determine which energy source offers the most sustainable outcome. A custom gas chamber was packed with Zeolite 13X and outfitted with four thermocouples. Following calibration, the device was connected to a carbon dioxide pump to measure and analyze the total adsorption level. Zeolite 13X effectively captured and released CO2 using all regeneration methods. The highest CO2 adsorption was observed using electric heat wrap (0.275 mmol CO2/g) and solar treatment (0.201 mmol CO2/g). Adsorption was greater in heat-assisted trials with Zeolite 13X compared to controls. Energy inputs remained consistent across sources: grid electricity (0.2653 MJ/kg CO2), solar (0.2642 MJ/kg CO2), and natural gas (0.2681 MJ/kg CO2). Grid electricity produced the lowest emissions per kilogram of CO2 captured (0.003 kg CO2 emitted/kg CO2), followed by solar (0.008 kg/kg), and natural gas (0.013 kg/kg). The experimental findings validate the energy-efficient nature of Zeolite 13X carbon capture technology which shows promise for agricultural and clean energy sector applications.

CO₂ Adsorption and Regeneration of Zeolite 13X: Energy Resource Comparison

Heart disease remains the leading global cause of death, highlighting the urgent need for accurate and scalable early detection methods. This study investigates the effectiveness of supervised machine learning techniques in predicting heart disease from routine clinical data. The objective is to determine which classification model provides the most reliable predictions in a clinical setting. We combined four public heart disease datasets from the UCI Machine Learning Repository and selected 14 relevant clinical features. Missing values were filled in using standard statistical methods (mean, median, or mode as appropriate). We then trained and evaluated four supervised machine learning models: logistic regression, support vector machine, extreme gradient boosting, and a multi-layer neural network. Each model was tuned for optimal performance and assessed using accuracy, precision, recall, and the F1-score. All models achieved higher than 80% accuracy. The support vector machine was the most accurate with 83.2% on the test data and also attained the highest F1-score (73%) for identifying heart disease cases. The neural network and gradient boosting models each reached about 82% accuracy, with the neural network identifying slightly more actual positive cases (higher recall). The logistic regression model trailed slightly at 80.7% accuracy but still showed competitive precision and recall. Machine learning models can effectively predict heart disease, providing a valuable tool for early detection. Among the models tested, the support vector machine performed best. These results indicate that support vector machines may offer stronger predictive performance for clinical applications of heart disease detection.

Evaluating Supervised Machine Learning Models for Heart Disease Prediction

Optical Coherence Tomography (OCT) is a widely adopted imaging technique in ophthalmology, yet its clinical interpretation remains prone to inconsistency and subjectivity. This study presents a retrieval-augmented generation (RAG) framework aimed at improving both diagnostic accuracy and interpretability in OCT-based analysis. The pipeline integrates a dual-headed convolutional neural network with a case-based retrieval system and a language model capable of generating structured clinical summaries. A ResNet-50 model was trained to classify diabetic retinopathy (DR), diabetic macular edema (DME), and key retinal biomarkers using a public OCT dataset. Feature embeddings generated by the model were indexed into a similarity-search database to enable retrieval of clinically relevant cases. Retrieved scans, along with classification outputs, were used to prompt a large language model to generate concise diagnostic reports that reflect both the model’s predictions and visually aligned precedents. The system achieved 89% accuracy in distinguishing DR from DME in a hold-out test set. Interpretability was also demonstrated through consistent retrieval of semantically similar cases. Generated reports were found to be readable and aligned with clinical reasoning. By combining classification performance with transparent, case-grounded explanations, the RAG approach enhances clinical confidence and supports decision-making. This work highlights the potential of retrieval-augmented pipelines to reduce variability in OCT interpretation and to serve as a scalable, explainable foundation for future AI-driven diagnostic support in ophthalmology.

Improving Optical Coherence Tomography Interpretation With Retrieval-Guided Diagnosis and Text Generation

As global energy demand rises and climate concerns intensify, solar energy systems or photovoltaic systems are becoming increasingly vital sources of renewable energy. However, their potential remains limited by the inability to operate naturally at the Maximum Power Point during hours of fluctuating sunlight. This study addresses this challenge by improving photovoltaic system efficiency through advanced Maximum Power Point Tracking techniques. We hypothesized that real-time duty cycle control would allow actual power to consistently rise towards Pmax, regardless of fluctuations. Using simulation software PowerSIM (PSIM), we analyzed the impact of varying duty cycles on power output across several trial runs. We conducted simulations on four PV panels, each with a distinct maximum power value, by modifying the duty cycle under five controlled power fluctuations designed to mimic variable sunlight. A code was developed and implemented to dynamically adjust the duty cycle based in response to changes in output power. The results showed that the PV panels with the highest (395W) and lowest (225W) Pmax values responded effectively, with actual power rising towards Pmax after each fluctuation. To validate our findings, we implemented the algorithm on a digital signal processing board using PSIM’s Processor-in-the-Loop module. The test completed in 4 minutes and 27 seconds, with the system's duty cycle varying with changes in actual power within a 0.03 range. The broader significance of this project lies in its contributions to developing responsive and cost effective PV systems that align with the goals of economic resilience and sustainable chronic disease management.

Improving Photovoltaic Systems for Renewable Energy Efficiency and Affordably Powering Aging Communities

This paper illustrates the process of transforming an initial mathematical permutation of numbers into a final permutation through a series of conditional swaps. The goal is to explore various approaches for modeling and analyzing all valid swap sequences that can achieve such a transformation. To address this and to better understand these transformations, different mathematical and combinatorial representations need to be studied, in order to gain useful insight into the underlying patterns of these transformations. This paper outlines a framework for understanding permutation transformations, solving problems modeled by similar rules, and provides grounds for further research in combinatorics and optimization.

Modelling Mathematical Permutation Transformations: Using Canonical Chains and Combinatorial Objects

Osteoarthritis (OA) is a prevalent and disabling condition affecting over one third of Canadians aged 65 and older, with the hand and wrist being among the most impacted joints. This chronic, degenerative joint disease severely limits physical independence by causing synovial inflammation and cartilage degradation, leading to joint stiffness. As a result, seniors diagnosed with OA often experience persistent joint pain, progressive joint immobility, and diminished quality of life. Current treatments such as pharmacotherapy and physiotherapy often provide limited or inconsistent relief due to side effects, accessibility, or patient adherence. In response, this project introduces a novel, multimodal therapeutic glove designed to deliver lasting non-pharmacological relief for individuals with hand and wrist OA. The glove integrates thermal stimulation and localized vibration to reduce pain and joint immobility, improving hand function. The design is grounded in an evidence-based understanding of osteoarthritis pain responses, with therapeutic principles derived from case studies and meta-analyses of both thermotherapy and massage therapy. The glove features vibrating mechanisms that simulate massage movements, providing relief and promoting circulation in affected areas. Furthermore, the glove incorporates heat therapy, which helps reduce stiffness and alleviates pain by increasing blood flow. To evaluate the glove’s effectiveness and usability, a pilot test was conducted with nine participants, where two-thirds of the participants reported hand or wrist pain. Guided sessions were used to measure pain and assess mobility using a numerical rating scale (0–5). The average reported pain relief score was 3.89, while the ease-of-movement score averaged 4.56, suggesting moderate to significant improvement.

Multimodal Wearable Devices: A Non-Pharmacological Treatment of Hand and Wrist Osteoarthritis

Arthritis is a leading cause of joint pain and reduced mobility, particularly among older adults. Current treatments still rely on reactive treatments and often do not offer personalized monitoring or early intervention. This paper addresses emerging wearable technology that could address these shortcomings by enabling more immediate and proactive management of joint pain. The primary objective was to design a low-cost, wearable knee brace system that integrates motion and pressure sensors with machine learning analytics to deliver real-time, personalized monitoring data for gait and joint stress. To achieve this, we conducted a comprehensive review of existing wearable sensor technologies and their applications in joint monitoring. Insights from this review guided the design of a prototype device featuring motion and pressure sensors embedded in an adjustable knee brace. We developed a companion mobile application to collect data, analyze joint health, and provide user-friendly feedback. Results demonstrated that the multi-sensor approach improved accuracy in tracking knee movement and pressure changes compared to single-sensor models. The device successfully streamed real-time data and classified gait patterns with promising accuracy. Our findings support the feasibility of this integrated system to enhance arthritis monitoring beyond current commercial options. This research contributes new knowledge by addressing gaps in existing wearable technologies, particularly for aging populations with arthritis. The proposed system offers a foundation for more personalized, preventative arthritis care that may improve quality of life and alleviate healthcare burdens for those with knee joint arthritis.

Proactive Arthritis Care with a Multi-Sensor Wearable Knee Brace System

Introduction: Acute interventions allow researchers to examine and compare the influence of a single exercise session. While researchers assume the washout period will minimize carryover effects between exercise bouts, early exercise adaptations may still take place, as observed within the repeated-bout effect. Rarely are these early adaptations assessed. This study aimed to assess pre-to-post acute intervention changes in body composition, strength, and cycling peak power output (Max PO) following an acute intervention of four identical exercise bouts. Methods: Eighteen untrained, healthy adults participated in both pre- and post-study testing. Weight and percent body fat were measured using a digital scale and air displacement plethysmography, respectively, pre- and post-study. Estimated 1 repetition maximums (e1RM) for leg press, chest press, seated row testing, and an incremental cycling test was conducted pre- and post-study. Paired t-tests were used to compare pre- and post-study outcomes in the overall group and stratified by sex. Results: Body weight increased overall (p=0.001), but sex-based analyses revealed an increase only in females (p=0.002). There was no change in percent body fat (p>0.05). e1RM increased for leg press, chest press and seated row as did cycling Max PO (p<0.05 for all), with increases in both sexes for leg press and chest press, and males only for seated row. Conclusions: The acute exercise intervention induced strength and performance improvements, indicating possible early adaptations to exercise. These adaptations may have implications on physiological outcomes measures and should be assessed in other acute interventions.

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