This non-invasive wearable integrates advanced photoplethysmography (PPG) sensing with a proprietary Pulse Shape Variability (PSV) algorithm to deliver real-time insights into stress levels linked to blood pressure fluctuations. Unlike conventional wearables that rely solely on heart rate or HRV, this technology analyzes the full morphology of the pulse waveform, capturing dynamic changes in amplitude, rise time, and contour that reflect vascular tone modulation caused by psychological stress. The result is a highly responsive and motion-tolerant stress detection platform that functions effectively in real-world conditions. By transforming microvascular signals into actionable insights, the solution enables proactive stress awareness, personalized wellness coaching, and context-aware emotional feedback, unlocking new opportunities in digital health, telemedicine, fitness, and mental wellness ecosystems. The technology owner is primarily seeking industry adopters and solution partners including medical institutions, device manufacturers, software developers, and fitness centers, who can integrate the technology into real-world applications with interest in deploying the system for use cases such as mental wellness, stress monitoring, fitness optimization, and remote healthcare. They also welcome collaboration with subject-matter experts to jointly enhance the algorithm and explore new features or application areas.

The adoption of multi-functional autonomous robots is steadily increasing to support and enhance operational efficiency in the facilities management sector. This technology presents a robot integrated with advanced sensor systems, artificial intelligence (AI), and autonomous mobility to perform multiple tasks. As a digital concierge, the robot provides enhanced visitor experience with seamless 2-way communication and an integrated touchscreen to connect with site duty personnel. The same screen can double up as an announcement board for advertisements and alerts, thereby extending a virtual front-desk capability effectively. In the security domain, this robot conducts autonomous patrols with real-time video surveillance and A.I.-based anomaly detection. The security head is embedded with a "brain” to perform on-edge computing to detect security-related used cases, significantly improving safety and accuracy in complex indoor environments. For cleaning, the robot can detect over 30 types of waste with 99% accuracy. Its self-adaptive cleaning system adjusts to floor type and debris volume, while a verification mechanism ensures more effective spot-cleaning compared to conventional single-pass robots.  These Multi-Functional Autonomous Facility Management Robot can yield significant operational savings, increase patrol frequency and shorten response time to incidents. This technology offers a software that features plug-and-play solutions that be customised to specific SOPs and needs. The technology owner is looking for collaborators, such as building owners and integrated facility management companies, with use cases to test-bed AI models. Examples include but not limited to identification of suspicious baggage, illegal parking or stray supermarket trolleys. 

This technology uses Machine Learning and AI algorithms to identify what appliances get plugged in to a building and when they are wasting energy. Plug Power represents 40% of the energy in a commercial building. Half of this energy is wasted with appliances left on when nobody is in the building. When wasted energy is found the plugs automatically switch off the appliances wasting energy and turn them back on before people return to the building. The technology not only saves energy and carbon emissions but makes buildings safer by detecting and preventing unsafe energy loads as well as reporting on occupancy and enabling behavioural change with occupants. The technology provider is seeking collaboration partners among businesses operating commercial buildings that utilize plug sockets — particularly those with multiple locations and high energy-consuming appliances. Potential partners include, but are not limited to, retail chains, F&B chains, the hospitality industry, healthcare facilities, education and training centres, and fitness and wellness chains.

Manual built environment inspection suffers from multiple issues such as shortage of manpower, human error and miscommunication. To overcome these issues, there is a need for an automated and centralized inspection system capable of detecting multiple defects of interest and presenting the inspection results in an easy to access format. The technology presented uses data acquired from LiDAR and Cameras mounted on an autonomous robot to inspect building interiors and external facades. The system utilizes an AI engine and can accurately detect defects such as cracks, holes, and other built imperfections stated in building quality guidelines such as CONQUAS. Defect reports can be autonomously generated after the acquired image and LiDAR data has been processed by the AI analytics engine. 

Vision-based Artificial Intelligence (AI) models require substantial time to train, fine-tune and deploy in production. After production, this process is still required when performance degrades and re-training on a new dataset becomes necessary; this maintenance process exists throughout the model's lifetime to ensure optimal performance. Rather than embarking on the time-consuming and painful process of collecting/acquiring data to train and tune the AI model, many organisations have turned to the use of pre-trained models to accelerate the AI model development process. This technology consists of a suite of pre-trained models that are intended to detect food, human behaviours, facial features and count people. These AI models are operable on video footage and static images obtained from cameras. Models are tuned and trained on various use-cases and are accessible via API calls or embedded within software as a Software Development Kit (SDK) library. These models can be deployed as AI as a Service on Microservices platform providing customer data protection with blockchain technology. With customer protection enhanced with blockchain technology, AI Model performance can further be enhanced to meet customer requirement.  

The Make Invisible Muscle Visible (MIMV) System is a non-invasive platform designed for full-spectrum muscle activity monitoring with high spatial and temporal precision. At its core lies a 20-channel high-density surface electromyography (HD-EMG) sensor array that wraps around a limb—such as the arm or leg—like a flexible brace, enabling comprehensive capture of both superficial and deep muscle activity. Real-time amplification and analog-to-digital conversion are embedded within the wearable unit, eliminating signal degradation and motion artifacts. This allows dynamic, wireless monitoring via Wi-Fi during actual movement, setting a new benchmark for muscle analytics in both research and applied settings. What sets the MIMV System apart is its ability to estimate deep muscle activation from surface-level data, enabled by a proprietary model-based signal processing algorithm. This makes the technology exceptionally versatile for diverse use cases—from analyzing intricate muscle coordination in elite athletes, to diagnosing and rehabilitating patients with neurological or movement disorders, to preserving and transmitting expert motor skills in traditional craftsmanship and skilled labor training. To maximize its potential impact, the technology owner is actively seeking collaborative partners such as clinical researchers in neurology and rehabilitation, companies developing wearable medical or sports performance technologies, and organizations dedicated to skilled labor education and movement training. These partners will play a critical role in co-developing real-world applications, validating performance in clinical and athletic contexts, and accelerating market deployment across healthcare, sports science, and vocational training sectors.

The Internet has become the de-facto medium for many enterprises to carry out their business functions. By relying on public-key encryption to ensure confidentiality and authenticity of data, employees and customers are able to use a variety of public channels via web browsers, emails and mobile apps to send and receive sensitive information securely. However, this promise of confidentiality and authenticity is being compromised with the advent of quantum computers. With the potential rise of exponentially powerful quantum computing, current data encryption algorithms are not resilient enough for such hidden quantum cyberattacks, specifically harvest-now decrypt-later (HNDL) attacks, resulting in data leaks and undermining privacy. The technology owner has leveraged on their proprietary post-quantum cryptography (PQC) implementation to develop a software module to provide and enhance existing end-to-end data encryption, ensuring resilience to quantum cyberattacks while maintaining confidentiality and authenticity of data. By utilising Key Encapsulation Mechanism (KEM) and JavaScript, it is compliant with evolving cybersecurity standards while being lightweight and dynamic enough to be loaded and executed without installation or configuration. This enables the technology solution to be flexible, scalable and user-friendly. The technology owner is currently working with an organisation to further develop industrial applicable solutions. The technology owner is seeking collaboration partners, such as system integrators, independent software vendors, solution providers and end-users, who require an enhanced and compliant data encryption for the finance, government and healthcare industries.

Reimagining how healthcare data is analyzed, this platform enables advanced data science without moving a single byte of patient data. Designed from the ground up to comply with global privacy regulations, it allows hospitals and research institutions to develop powerful AI models using real-world clinical data—while keeping that data entirely on-site under governance. Powered by proprietary federated learning and a modular AI/ML development toolkit, the platform solves one of healthcare’s biggest bottlenecks: secure access to data for innovation. With seamless integration into existing IT systems and end-to-end compliance with GDPR and HIPAA, this is the infrastructure for the future of privacy-first, multi-institutional healthcare data analytics and AI/ML. The technology owner is actively seeking collaboration with healthcare providers, research institutions, clinical research organizations, and data-centric health-tech companies eager to scale innovation—without compromising privacy or control.

With the focus on food security and sustainability, urban farming is gaining traction in Singapore. Most farmers who are new to the industry still rely heavily on pesticides for pest control and crop protection. Although effective, the use of pesticides pose several concerns such as: Health and Environmental Risks: Pesticide residues in food pose potential health hazards, while excessive use can lead to soil degradation, water contamination, and harm to beneficial insects and biodiversity. Regulatory and Market Pressure: Stricter regulations on pesticide use and growing demand for organic and naturally grown produce create the need for sustainable solutions. In addition, the educated consumers increasingly prefer pesticide-free vegetables, thereby driving the need for natural alternatives. Biological solutions such as biofertilizers, bionutrients, and biostimulants offer a promising alternative to agriculture. These solutions can: Enhance Plant Growth: Improve nutrient uptake and plant resilience through naturally derived growth stimulants. Improve Soil Health: Support microbial activity and enhance soil fertility without chemical degradation. Reduce Dependence on Chemical Fertilizers and Pesticides: Provide natural pest resistance and nutrient supply. Promote Sustainable Production of Asian Leafy Greens: Crops such as bak choy, butterhead lettuce, and spinach Farmers are seeking to collaborate with companies with biological solutions to run validation trials in their farms.

Agricultural activities in Asia Pacific region generate significant amounts of manure from various farming operations, including: Layer Hens: A layer hen produces approximately 0.08–0.12 kg of manure per day. Cattle Farming: Dairy and beef cattle generate an average of 30 kg of manure per day per animal. Pig Farms: Pigs produce about 2–4 kg of manure per day. Manure accumulation presents several environmental and operational challenges such as odour pollution, water contamination, greenhouse gas emissions and pathogen proliferation. Despite these challenges, manure has significant potential when processed effectively. It can be converted into valuable resources such as: Biogas & Bioenergy Biochar & Carbon Sequestration Organic Fertilizers and Soil Conditioners Farms have found solutions that produce good quality fertilisers and soil conditioners with their manure. However the amount of manure generated is more than what they can use and would like to work with innovation technologies to manage and convert their farm's manure efficiently. 

According to the Health Promotion Board (HPB) in Singapore, 90% of Singapore residents' consumption of sodium exceeds the daily recommended intake, with the average consumption at 3620 mg, nearly double of the recommended 2000 mg per day. As a result, one in six Singapore residents report health problems like hypertension and hypolipidaemia. In an effort to curb this, HPB launched the "Less Salt, More Taste" campaign in 2024 to encourage the food manufacturing and food service industry to change their recipes and add less salt and sauces, or switch to use lower-sodium alternatives for salt, sauces and seasonings. Manufacturers are also encouraged to increase the supply of lower-sodium ingredients for the food service sector. Consumers are also encouraged to choose low sodium options, or use less salt in their home cooking. This sodium reduction strategy aims to reduce Singaporeans’ sodium intake by 15% by 2026.