Technologies for older adults largely focus on monitoring, safety, and functional support, with less attention to everyday emotional meaning-making. Reflective practices can support emotional wellbeing in later life, yet reflective tools tailored to older adults, particularly in India, remain limited. Self-perceptions of aging (SPA) are strongly linked to emotional wellbeing, but SPA work largely relies on questionnaires and clinical formats rather than culturally grounded tools for reflection. This pictorial presents a research-through-design process leading to a culturally grounded reflection tool for older adults in India. It combines SPA-based reflection cards and prompts with non-diagnostic reflective guidance drawn from a curated corpus rooted in cultural understanding. The work co-designed SPA reflection cards grounded in older adults’ metaphors and narratives, and developed the guidance system with older adults and domain experts. It contributes design knowledge on operationalizing SPA in culturally attuned, technology-mediated reflective tools for older adults in urban India.

Modern eye tracking is a fundamental tool for understanding how humans distribute visual attention during complex cognitive tasks. However, traditional metrics often fail to capture whether a user is attending to the specific semantic information required to solve a problem. Here, we present a multimodal framework that leverages large language models to generate benchmark semantic attention maps, enabling automated alignment and correction of user attention. Using a curated dataset of 37 diverse visual stimuli, we demonstrate that this framework successfully identifies discrepancies between a user’s explored areas and semantically relevant regions. This work confirms that combining gaze coordinates with AI-generated heatmaps enables the system to provide granular insights and that automated feedback can help improve visual exploration strategies. This methodology provides a scalable foundation for enhancing human performance in visual reasoning, extending eye-tracking research from simple text-based paradigms to complex, infographic-based environments.

Brain-machine interfaces (BMIs) have significantly advanced neuro-rehabilitation by enhancing motor control. However, accurately decoding continuous grasp force remains a challenge, limiting the effectiveness of BMI applications for fine motor tasks. Current models tend to prioritise algorithmic complexity rather than incorporating neurophysiological insights into force control, which is essential for developing effective neural engineering solutions. To address this, we propose EEGForceMap, an EEG-based methodology that isolates signals from the premotor-parietal region and extracts task-specific components. We construct three distinct time-frequency feature sets, which are validated by comparing them with prior studies, and use them for force prediction with linear, nonlinear, and deep learning-based regressors. The performance of these regressors was evaluated on the WAY-EEG-GAL dataset that includes 12 subjects. Our results show that integrating EEGForceMap approach with regressor models yields a 61.7% improvement in subject-specific conditions (R2= 0.815) and a 55.7% improvement in subject-independent conditions (R2= 0.785) over the state-of-the-art kinematic decoder models. Furthermore, an ablation study confirms that each preprocessing step significantly enhances decoding accuracy. This work contributes to the advancement of responsive BMIs for stroke rehabilitation and assistive robotics by improving EEG-based decoding of dynamic grasp force.

Legacy floor plans, often preserved only as scanned documents, remain essential resources for architecture, urban planning, and facility management in the construction industry. However, the lack of machine-readable floor plans render large-scale interpretation both time-consuming and error-prone. Automated symbol spotting offers a scalable solution by enabling the identification of service key symbols directly from floor plans, supporting workflows such as cost estimation, infrastructure maintenance, and regulatory compliance. This work introduces a labelled Digitised Electrical Layout Plans (DELP) dataset comprising 45 scanned electrical layout plans annotated with 2,450 instances across 34 distinct service key classes. A systematic evaluation framework is proposed using pretrained object detection models for DELP dataset. Among the models benchmarked, YOLOv8 achieves the highest performance with a mean Average Precision (mAP) of 82.5%. Using YOLOv8, we develop SkeySpot, a lightweight, open-source toolkit for real-time detection, classification, and quantification of electrical symbols. SkeySpot produces structured, standardised outputs that can be scaled up for interoperable building information workflows, ultimately enabling compatibility across downstream applications and regulatory platforms. By lowering dependency on proprietary CAD systems and reducing manual annotation effort, this approach makes the digitisation of electrical layouts more accessible to small and medium-sized enterprises (SMEs) in the construction industry, while supporting broader goals of standardisation, interoperability, and sustainability in the built environment.

Over the past decade, the demand for communication devices has increased among individuals with mobility and speech impairments. Eye-gaze tracking has emerged as a promising solution for hands-free communication; however, traditional appearance-based interfaces often face challenges such as accuracy issues, involuntary eye movements, and difficulties with extensive command sets. This work presents a multimodal appearance-based gaze-controlled virtual keyboard that utilises deep learning in conjunction with standard camera hardware, incorporating both synchronous and asynchronous modes for command selection. The virtual keyboard application supports menu-based selection with nine commands, enabling users to spell and type up to 56 English characters—including uppercase and lowercase letters, punctuation, and a delete function for corrections. The proposed system was evaluated with twenty able-bodied participants who completed specially designed typing tasks using three input modalities: (i) a mouse, (ii) an eye-tracker, and (iii) an unmodified webcam. Typing performance was measured in terms of speed and information transfer rate (ITR) at both command and letter levels. Average typing speeds were 18.3±5.31 letters/min (mouse), 12.60±2.99 letters/min (eye-tracker, synchronous), 10.94±1.89 letters/min (webcam, synchronous), 11.15±2.90 letters/min (eye-tracker, asynchronous), and 7.86 ± 1.69 letters/min (webcam, asynchronous). ITRs were approximately 80.29±15.72 bits/min (command level) and 63.56±11 bits/min (letter level) with webcam in synchronous mode. The system demonstrated good usability and low workload with webcam input, highlighting its user-centred design and promise as an accessible communication tool in low-resource settings.

Brain-computer interface (BCI) systems facilitate unique communication between humans and computers, benefiting severely disabled individuals. Despite decades of research, BCIs are not fully integrated into clinical and commercial settings. It’s crucial to assess and explain BCI performance, offering clear explanations for potential users to avoid frustration when it doesn’t work as expected. This work investigates the efficacy of different deep learning and Riemannian geometry-based classification models in the context of motor imagery (MI) based BCI using electroencephalography (EEG). We then propose an optimal transport theory-based approach using earth mover’s distance (EMD) to quantify the comparison of the feature relevance map with the domain knowledge of neuroscience. For this, we utilized explainable AI (XAI) techniques for generating feature relevance in the spatial domain to identify important channels for model outcomes. Three state-of-the-art models are implemented - 1) Riemannian geometry-based classifier, 2) EEGNet, and 3) EEG Conformer, and the observed trend in the model’s accuracy across different architectures on the dataset correlates with the proposed feature relevance metrics. The models with diverse architectures perform significantly better when trained on channels relevant to motor imagery than data-driven channel selection. This work focuses attention on the necessity for interpretability and incorporating metrics beyond accuracy, underscores the value of combining domain knowledge and quantifying model interpretations with data-driven approaches in creating reliable and robust Brain-Computer Interfaces (BCIs).

Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-Computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about x 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.

On-screen keyboard eye-typing systems are limited due to the lack of predictive text and user-centred approaches, resulting in low text entry rates and frequent recalibration. This work proposes integrating the prediction by partial matching (PPM) technique into a tree-based virtual keyboard. We developed the Flex-Tree on-screen keyboard using a two-stage tree-based character selection system with ten commands, testing it with three degree of PPM (PPM1, PPM2, PPM3). Flex-Tree provides access to 72 English characters, including upper- and lower-case letters, numbers, and special characters, and offers functionalities like the delete command for corrections. The system was evaluated with sixteen healthy volunteers using two specially designed typing tasks, including the hand-picked and random-picked sentences. The spelling task was performed using two input modalities: (i) a mouse and (ii) a portable eye-tracker. Two experiments were conducted, encompassing 24 different conditions. The typing performance of Flex-Tree was compared with that of a tree-based virtual keyboard with an alphabetic arrangement (NoPPM) and the Dasher on-screen keyboard for new users. Flex-Tree with PPM3 outperformed the other keyboards, achieving average text entry speeds of 27.7 letters/min with a mouse and 16.3 letters/min with an eye-tracker. Using the eye-tracker, the information transfer rates at the command and letter levels were 108.4 bits/min and 100.7 bits/min, respectively. Flex-Tree, across all three degree of PPM, received high ratings on the system usability scale and low-weighted ratings on the NASA Task Load Index for both input modalities, highlighting its user-centred design.

The high burden of lung diseases on healthcare necessitates effective detection methods. Current Computer-aided design (CAD) systems are limited by their focus on specific diseases and computationally demanding deep learning models. To overcome these challenges, we introduce CNN-O-ELMNet, a lightweight classification model designed to efficiently detect various lung diseases, surpassing the limitations of disease-specific CAD systems and the complexity of deep learning models. This model combines a convolutional neural network for deep feature extraction with an optimized extreme learning machine, utilizing the imperialistic competitive algorithm for enhanced predictions. We then evaluated the effectiveness of CNN-O-ELMNet using benchmark datasets for lung diseases: distinguishing pneumothorax vs. non-pneumothorax, tuberculosis vs. normal, and lung cancer vs. healthy cases. Our findings demonstrate that CNN-O-ELMNet significantly outperformed (p < 0.05) state-of-the-art methods in binary classifications for tuberculosis and cancer, achieving accuracies of 97.85% and 97.7%, respectively, while maintaining low computational complexity with only 2481 trainable parameters.We also extended themodel to categorize lung disease severity based on Brixia scores. Achieving a 96.2% accuracy in multi-class assessment for mild, moderate, and severe cases, makes it suitable for deployment in lightweight healthcare devices.

Children with dyslexia need specific instructions for spelling and word analysis from an early age. It is important to provide appropriate tools using technology for writing aids to such children that can help them to input text, while providing multiple feedback. However, it is unclear how children with dyslexia can efficiently use a gaze-based virtual keyboard (VK). In this study, we propose to use the typing performance of a multimodal Hindi language eye-gaze-assisted learning system based on a VK to help in the reduction of tracking errors for people with writing and reading deficiencies and to detect children with dyslexia. Performance was assessed at three levels: eye tracker, eye tracker with soft switch, and touchscreen as a baseline modality using a predefined copy-typing task. The system was validated through a series of experiments with 32 children (16 dyslexic and 16 control). The results show that the workload and the usability of the system are substantially different for children with dyslexia. Children with dyslexia have a lower typing performance when using the touchscreen modality or the eye tracker only. The detection of children with dyslexia from others was assessed with seven different types of classifiers using the typing speed on different words (AUC$>$0.9). These results highlight the need to have fully inclusive VKs. This work demonstrates the superior use of a multimodal system with participants having unique neuropsychological conditions and that the proposed system can be used to detect children with dyslexia.

Many of us daily encounter shadow and reflected light patterns alongside macro-level changes in ambient light levels. These are caused by elements—opaque objects, glass, mirrors, even clouds—in our environment interfacing with sunlight or artificial indoor lighting. Inspired by these phenomena, we explored ways of creating digitally-supported displays that use light, shade and reflection for output and harness the energy they need to operate from the sun or indoor ambient light. Through a set of design workshops we developed exemplar devices: SolarPix, ShadMo and GlowBoard. We detail their function and implementation, as well as evidencing their technical viability. The designs were informed by material understandings from the Global North and Global South and demonstrated in a cross-cultural workshop run in parallel in India and South Africa where community co-designers reflected on their uses and value given lived experience of their communication practices and unreliable energy networks.

Working with emergent users in two of Mumbai’s slums, we explored the value and uses of photovoltaic (PV) self-powering digital materials. Through a series of co-design workshops, a diary study and responses by artists and craftspeople, we developed the PV-Pix concept for inter-home connections. Each PV-Pix element consists of a deformable energy harvesting material that, when actuated by a person in one home, changes its physical state both there and in a connected home. To explore the concept we considered two forms of PV-Pix: one uses rigid materials and the other flexible ones. We deployed two low-fidelity prototypes, each constructed of a grid of one PV-Pix type, in four slum homes over a four week period to further understand the usability and uses of the materials, eliciting interesting inter-family communication practices. Encouraged by these results we report on a first-step towards working prototypes and demonstrate the technical viability of the approach.

The interactive, digital future with its seductive vision of Internet-of-Things connected sensors, actuators and displays comes at a high cost in terms of both energy demands and the clutter it brings to the physical world. But what if such devices were made of materials that enabled them to self-power their interactive features? And, what if those materials were directly used to build aesthetically pleasing environments and objects that met practical physical needs as well as digital ones? In this paper we introduce PV-Tiles ? a novel material that closely couples photovoltaic energy harvesting and light sensing materials with digital interface components. We consider potential contexts, use-cases and light gestures surfaced through co-creation workshops; and, present initial technological designs and prototypes. The work opens a new set of opportunities and collaborations between HCI and material science, stimulating technical and design pointers to accommodate and exploit the material's properties.

Appropriately combining mental practice (MP) and physical practice (PP) in a poststroke rehabilitation is critical for ensuring a substantially positive rehabilitation outcome. Here, we present a rehabilitation protocol incorporating a separate active PP stage followed by MP stage, using a hand exoskeleton and brain-computer interface (BCI). The PP stage was mediated by a force sensor feedback-based assist-as-needed control strategy, whereas the MP stage provided BCI-based multimodal neurofeedback combining anthropomorphic visual feedback and proprioceptive feedback of the impaired hand extension attempt. A six week long clinical trial was conducted on four hemiparetic stroke patients (screened out of 16) with a left-hand disability. The primary outcome, motor functional recovery, was measured in terms of changes in grip-strength (GS) and action research arm test (ARAT) scores; whereas the secondary outcome, usability of the system was measured in terms of changes in mood, fatigue, and motivation on a visual-analog-scale. A positive rehabilitative outcome was found as the group mean changes from the baseline in the GS and ARAT were +6.38 kg and +5.66 accordingly. The VAS scale measurements also showed betterment in mood ( 1.38), increased motivation (+2.10) and reduced fatigue (0.98) as compared to the baseline. Thus, the proposed neurorehabilitation protocol is found to be promising both in terms of clinical effectiveness and usability.

People suffering from a variety of upper and lower limb disabilities due to different neuro-muscular diseases or injuries, often find it difficult to perform day-to-day activities of mobility and grasping (pick and place) objects. This paper presents the feasibility and utility of a newly developed assistive device named EMOHEX, for disabled people to perform some activities of daily living (ADL). EMOHEX is an integrated platform that combines a low cost eye-tracking device with a powered-wheelchair mounted hand-exoskeleton, which can assist disabled people in grasping objects while moving around. A dual control panel based graphical user interface is designed wherein the user's intention to select any command button is detected through eye-tracking. The dual control consists of wheelchair control panel and exoskeleton control panel, which are interchangeable by a switch button common to both the panels. The hand-exoskeleton is capable of assisting grasp, hold, and release action. Experiments conducted on 16 healthy participants revealed that performance metrics were significantly (p<;0.01) similar for the same task complexity while for different task complexities the performance metrics were significantly (p<;0.01) different across all the participants. These results showed the feasibility and stability of the system, respectively. Moreover, the information transfer rate (ITR) of eye-tracker was found satisfactory at 55.28±1.29 bits/min and 51.02±1.72 bits/min for simple and complex task, respectively. Thus, EMOHEX has the potential as a quality assistive device for disabled people.

Over the last decade, there has been a gradual increase in the number of people with mobility and speech impairments who require novel communication devices. Most of the recent works focus on the Latin script; there is a lack of appropriate assistive devices for scripts that are specific to a country. In this paper, we propose a novel multimodal Hindi language virtual keyboard based on a menu selection with eight commands providing access to spell and type 63 different Hindi language characters along with other functionalities such as the delete command for corrections. The system has been evaluated with eight able-bodied individuals who performed a specially designed typing task. The spelling task has been achieved in three different modalities using: (i) a mouse, (ii) a portable eye-tracker, and (iii) a portable eye-tracker combined with a soft-switch. The performance has been evaluated over the changes that occur with the use of each modality in terms of typing speed and information transfer rate (ITR) at both the command and letter levels for each participant. The average speed across participants with mouse only, eye-tracker only, and eye-tracker with soft-switch were 17.12 letters/min, 10.62 letters/min, and 13.50 letters/min, respectively. The ITRs at the command and letter levels were about 67.58 bits/minute and 62.67 bits/minute, respectively, with only the eye-tracker option. Based on its robustness, the proposed system has the potential to be used as a means of augmentative communication for patients suffering from mobility and speech impairment, and can contribute to substantial improvement in their quality of life.

Non-invasive Brain-Computer Interface (BCI) has appeared as a new hope for a large population of disabled people, who were waiting for a new communication means that would translate some brain responses into actions. After several decades of research in fields such as neuroscience and machine learning, the performance remains too low due to the low signal to noise ratio of the EEG signal, and the time that has to be dedicated to the recording of the brain responses. Hybrid BCIs consider the combination of several modalities, including brain responses, for new communication systems. The creation of a Hybrid BCI requires particular care as it possesses the constraints from several modalities. We propose to investigate the performance that could be achieved in a paradigm, where gaze control is used for the selection of an item on a computer screen and motor imagery is used to enable the selected item on the screen. Based on the results obtained from gaze detection with an eye tracker, and motor imagery detection with non-invasive EEG recording, we show that the performance of a parallel Hybrid BCI is only beneficial if the accuracy of each modality reaches a particular limit, and if the number of commands from each modality is carefully chosen.