The Hanumayamma Hayagriva (HG Model 3) is a next-generation, high-precision, non-contact sensor engineered to advance racehorse safety, wellness, and performance. Seamlessly embedded within the racing saddle, it captures detailed motion and stride analytics - without ever touching the horse’s skin - ensuring maximum comfort, precision, and integration.
Powered by advanced electronics and AI-driven analytics, the HG Sensor continuously tracks micro-movements and stride variations, identifying early signs of stress, fatigue, or injury risk long before they impact performance or health.
Drawing its name from “Hayagriva,” the horse-headed avatar of Lord Vishnu, the system embodies wisdom, strength, and protection—values reflected in its mission to safeguard equine athletes through innovation and compassion.
The HG Sensor applies real-time data science and artificial intelligence to detect anomalies and biomechanical outliers, mapping them to clinical and biomedical parameters across multiple harmonics. With sub-millisecond precision, it captures gait and motion dynamics at extraordinary resolution.
Through a real-time, closed-loop feedback system, the sensor delivers actionable insights directly to veterinarians, trainers, and horse owners, offering instant health alerts and predictive recommendations.
Using time-series analysis and AI-based pattern mining - including hopping, tumbling, session, and sliding windows - the system detects recurring patterns, emerging risks, and evolving performance trends.
By combining stride analytics with association rule mining, the HG Sensor predicts future behaviors and potential health events. These prognostic insights empower caretakers to intervene early, preserve horse health, and enhance racing performance.
The Hanumayamma Hayagriva (HG Model 3) stands as a breakthrough in equine AI and sensor technology, transforming every stride into actionable intelligence—ensuring that every racehorse runs not just faster, but safer.
The Hanumayamma HG Sensor features high-resilience electronics engineered to capture a horse’s accelerative and stride movements with sub-millisecond precision. Each motion event is intelligently bucketed into categorized safety zones, allowing the system to identify patterns that signal early signs of strain or imbalance.
Through advanced in-memory analytics, including snapshot normalization and stride-window processing, the sensor transforms raw movement data into prognostic health markers. An integrated AI engine evaluates these markers, classifying and forecasting potential safety-related events before they occur.
To support real-time decisioning, the HG Sensor is equipped with high-capacity onboard memory and storage, optimized for continuous operation. Despite its advanced capabilities and energy efficiency, the fully powered sensor weighs less than one gram, ensuring zero interference with performance and total comfort for the horse.
Banks & Insurance Companies Prognostic Analytics to help Banks and Insurance managers to better plan mitigating horse safety issues.
AI That Protects Every Stride Hanumayamma EquineAnalytics™ empowers trainers with real-time AI insights to enhance performance, prevent injuries, and ensure racehorse longevity
Veterinarians & Research Laboratories Veterinarians gain real-time diagnostic insights, enabling early detection of stress or injury and data-driven care for optimal equine health.
Hanumayamma is a leader in developing Machine Learning–based analytics platforms that harness data from our Dairy Cow Necklace Sensors, Hanumayamma Hayagriva (HG Model 3) Horse Sensors, and geolocation data, along with global datasets such as those from the FAO. Our advanced Dairy and Equine Analytics solutions use AI-driven insights to empower farmers, veterinarians, animal husbandry experts, and agricultural companies to engage with their customers in smarter, more efficient, and disruptive ways. For example, Connected Dairy Analytics and Connected Equine Analytics—our big data platforms—deliver operational efficiencies, cost savings, predictive health monitoring, and actionable insights to address critical issues in dairy and equine management. These data-enabled tools are redefining how livestock health, performance, and productivity are managed—ensuring healthier animals, safer operations, and more sustainable outcomes for the global agriculture ecosystem.
Hanumayamma Equine Analytics – Hayagriva (HG Model 3) Prognostic Framework 
Hanumayamma Hayagriva (HG Model 3) Precision Engineering for Equine Safety and Performance - Intelligent Precision for Racehorse Safety and Performance
Safety Prognastic Alerts The HG Sensor applies real-time data science and artificial intelligence to detect anomalies and biomechanical outliers, mapping them to clinical and biomedical parameters across multiple harmonics. With sub-millisecond precision, it captures gait and motion dynamics at extraordinary resolution.
A non-touch Veterinary Sensor We are pleased to announce that Hanumayamma Innovations and Technologies, Inc. has received its product classification trademark (™) from the United States Patent and Trademark Office (USPTO). This officially designates us as a Class 10 device manufacturer — a category reserved for medical and veterinary devices, including surgical instruments, diagnostic tools, orthopedic products, and wearable health technologies. Specifically, we have been granted the designation: “Class 10: Wearable veterinary sensor for use in capturing a horse’s vital signs, stride patterns, and biomechanical movements—providing data to trainers, veterinarians, and owners to monitor performance, detect fatigue or injury risks, and improve overall equine health and safety.” This milestone reinforces Hanumayamma’s leadership in AI-driven, non-invasive equine health monitoring and its commitment to precision engineering for animal wellness and performance optimization.
Waterproof Sensor Enclosure We found out from our field analysis that Dairy IoT sensors work better and last longer with a waterproof sensor enclosure; this way, dairy management can keep doing their usual day to day activities or process without any changes.
Edge Module The Sensor Edge Module allows real-time and useful insights for dairy management to be delivered. We have developed the Sensor Edge Module to work with low and intermittent network connections, so that our analytics can offer the most precise and relevant insights for the dairy industry.
Sustainability Platform Hanumayamma Innovations and Technologies, Inc. offers a Sustainability Platform for reporting and analytics, with a focus on agriculture and dairy industries. Hanumayamma Environmental, Social, and Governance (ESG) Platform - Designed to help dairy and agricultural farms, cooperatives, and food processing units of different sizes achieve their environmental sustainability goals and improve the world.
Data and Analytics Data and analytics are at the core of Hanumayamma’ s approach to accelerating sustainability progress. Hanumayamma Sustainability Platform uses Hanumayamma Dairy & Agriculture Cloud to gather data from various dairy and agricultural sensors, such as Cow Necklace, Class 10, veterinarian diagnostic sensor, and creates a common data model. It then converts different data to provide insights on Water, Methane, Milk, and Green Analytics and helps farmers to act on them.
Hardware that captures Methane emissions from Small, Medium, and Large Dairy Farms and upload to Hanumayamma Cloud.
Sustainability Manager Hanumayamma’ s Sustainability Manager empowers organizations to turn data insights into action and reduce environmental impact. Hanumayamma’ s platform allows organizations to calculate their sustainability footprint, analyze ESG performance against goals, and provide data governance. This is how platform process data: first, it uses Hanumayamma Cloud for Sustainability data model, which enhances field sensor data, weather & satellite, and econometric data. Second, the data is analyzed with Hanumayamma’ s data science algorithms to produce ESG calculations and tracking for each tenant. Lastly, it produces customized data insights for compliance and governance reporting purposes. Actionable steps and progress towards objectives are the outcomes that help us & world achieve ESG goals.
Improve dairy cattle health Attention small and marginal dairy farmers! Are you struggling with cattle productivity and health issues? Do you want to improve your dairy cattle's health and productivity while also contributing to mitigating climate change? Look no further than our Hanumayamma Dairy Cattle Cow Necklace sensors! By tracking cattle rumination counts, our sensors help identify cows experiencing digestive issues or other health problems and adjust their feeding and management practices accordingly. Join the global efforts to reduce methane emissions and improve dairy cattle health with our innovative technology!Product Features:
Data and Analytics Data and analytics are at the core of Hanumayamma’ s approach to accelerating sustainability progress. Hanumayamma Sustainability Platform uses Hanumayamma Dairy & Agriculture Cloud to gather data from various dairy and agricultural sensors, such as Cow Necklace, Class 10, veterinarian diagnostic sensor, and creates a common data model. It then converts different data to provide insights on Water, Methane, Milk, and Green Analytics and helps farmers to act on them.
Hardware that captures Methane emissions from Small, Medium, and Large Dairy Farms and upload to Hanumayamma Cloud. Sustainability Manager Hanumayamma’ s Sustainability Manager empowers organizations to turn data insights into action and reduce environmental impact. Hanumayamma’ s platform allows organizations to calculate their sustainability footprint, analyze ESG performance against goals, and provide data governance. This is how platform process data: first, it uses Hanumayamma Cloud for Sustainability data model, which enhances field sensor data, weather & satellite, and econometric data. Second, the data is analyzed with Hanumayamma’ s data science algorithms to produce ESG calculations and tracking for each tenant. Lastly, it produces customized data insights for compliance and governance reporting purposes. Actionable steps and progress towards objectives are the outcomes that help us & world achieve ESG goals.
Milk Fever Milk fever is a condition that commonly occurs in dairy cows near the time of giving birth. It is a metabolic disorder that results from a lack of calcium in the blood (hypocalcaemia). It affects between 3 to 10% of cows in dairy areas each year, and some farms have much higher rates.
Ketosis Ketosis is a condition in cattle where energy needs-- for instance, the higher output of milk-- are higher than energy consumption, leading to a reduced, or “negative” energy balance.
Our Sensors identify onset of Ketosis Symptoms: Rumination Rumination is the natural process of bringing fibrous food from the rumen to the mouth and back to the rumen in cattle and other animals called ruminants. Rumination is not only essential for healthy cows; it can also signal stress or sickness early on. Rumination is influenced by both diet-related factors-- such as how much fiber is in a feed, the form of fiber, and how well it is digested - and how the rumen works internally, but a cow will adjust rumination according to external stressors on any given diet. Stress can be measured by how much rumination time changes from the herd’s or the individual cow’s normal rumination time, 48 hours before other signs show up. By tracking drops in rumination times, management changes can be done to reduce stressful situations, or to start treating a sick cow. Our sensors allow farmers to set and capture rumination threshold counts, which help them spot possible changes in rumination patterns, and treat cattle accordingly.
Lameness Lameness makes cattle suffer, which weakens them and lowers productivity. The financial impact of lameness involves losses from reduced production, treatment expenses, longer calving interval, and possibly extra labor. Less milk of 1.7 - 3 L/day for up to 1 month before and 1 month after treatment due to pain, and milk wasted because of antibiotic therapy must also be taken into account. Our sensors detect lameness signs, and help farmers to deal with them appropriately. Our Sensors detect Lameness (proactive) Signs:
Healthy vs. Sick Cattle Detection Our Sensors identify healthy-vs.-sick cattle:
Predicting the prices of commodities is crucial for making choices about how much land to plant or harvest crops on and the economic health of small farmers. The anticipated prices of agricultural commodities can affect the farming and ranching decisions of how much land to use for crops or how many animals to keep and, as a result, impact the availability of agricultural commodities.
A farm’s financial health is also influenced by changes in commodity prices. For example, when commodity prices are low for a long time, farm incomes go down and farmers depend more on credit, making them more exposed to higher interest rates and other economic shifts. When commodity prices are high for a long time, farm incomes go up and farmers become more resilient to economic changes. Changes to commodity prices also affect food security: when prices are low for a long time, consumers can buy enough food more easily, while when prices are high for a long time, their food security is reduced, especially in developing countries.
Agricultural product prices have some unique characteristics and need special attention. Agricultural Commodity Markets are affected by macroeconomic environment, oil prices, supply/demand, consumer tastes/preferences, bad weather conditions, biofuels, stock to use ratios, dollar exchange rates, speculation, food storage, speculative activity, financial markets, fertilizers, trade restrictions, wealth of nations, and other economic factors.
The Hanumayamma Analytics platform examines the credit given to agriculture by the private/commercial banking sector in over 120 countries, which includes loans to producers in agriculture, forestry and fisheries, such as household producers, cooperatives, and agro-businesses. The Hanumayamma Agriculture Analytic platform uses statistical techniques and econometric models to generate customized recommendations for local farmers, with the aim of providing advanced insights to cope with any changes in macroeconomic conditions, while ensuring the profitability of small farmers. The Hanumayamma agriculture platform relies on credit to agriculture data for its Commodity Models and Risk Models, which constantly evaluate global market conditions to protect the small farmer.
Analytics platform consists of two core engines: Machine Learning and Recommendation Engine
The goal of our ML and rule processing is simple: provide actionable insights to dairy farmers.
Technologically, ML and rule processing are performed at both a dairy sensor level (closer to
cattle in dairy farms) and in the Hanumayamma Dairy Cloud.
Pattern Detection: We have created various supervised and unsupervised machine learning algorithms as part of the Dairy Analytics platform. We have examined several dairy streams in real-time and built decision trees that follow the industry standards and can use the best proprietary machine learning algorithms ( USPTO - Patent Pending ). We can use Machine Learning and Pattern detection to find out how different dairy optimization factors are related, such as how medication, vitamin intake, output, and seasonal factors affect the health and milk production of dairy cows.
Historical Analysis: Historical analysis considers how seasonal factors like heat stress (HS) and pneumonia affect the milk production of dairy cattle. Also, our architecture connects the weather forecast-- that is, the expected weather conditions in the next weeks-- to the historical observed responses of dairy cattle. For example, if dairy cattle exhibit signs of sickness due to rapid temperature changes, our architecture offers practical advice to dairy staff by checking for any sudden or future weather changes and promptly notifying them of any such events.
We provide a dairy recommendation system that helps manage food optimization, medication suggestions, optimal dairy settings per cattle, and disease management. We have created a dairy recommendation engine that combines various data sources that account for both dairy context and location details (USPTO - Patent Pending)
We use both content and collaborative recommendation systems. The main goal of the collaborative recommendation method is to create network benefits in terms of cattle disease analysis, medication, and milk productivity.
Smary Notifications: One of the key aspects of our recommendation system is to rank the notifications that dairy management and operation staff get and show them in a way that prevents alert overload. We have studied different dairy farms - big and small - where systems such as milk production, weather, temperature, health, and location generate many notifications per day; this can disrupt dairy operations, and therefore lower overall productivity, if not managed well.
Social presence Analytics: Our recommendation system uses location based social presence analytics to deliver the most useful information for dairy management. By using social presence analytics for dairy management, cattle health management becomes much better, and, at the same time, dairy farms can work together with the local community.
Farming is a costly process that involves both internal and external costs; some of these costs are feed, fertilizers, human labor, equipment, finance, operations, security, and transportation. Fertilizer is a major cost for most grain farms, but its percentage as a crop cost varies among continents and countries. For instance, fertilizer is the main source of serious debt for small farms in India. Also, the price of agricultural fertilizers has a huge impact on the total yield and production of maize in Malawi.
To forecast the price of fertilizer would benefit developing economies in many ways, and also have a life-saving impact on them. For example, fertilizer price prediction would lower the debt of farmers. A 2018 study by the National Bank for Agriculture and Rural Development in India found that 52.5 percent of all agricultural households had debt, according to current banking rules that prevent farmers with existing loans from getting more credit. Therefore, indebted families who need credit often force female farmers to take loans, which worsens the debt cycle that is very common in the western Indian region and in many developing countries where farms have fallen into cyclic debt due to the cost of fertilizers and low crop yields. According to data that came out earlier this year, India had 93 million microfinance accounts, mostly belonging to women in self-help groups; an increase of 22 percent from the year before. Microfinance lending rates are much lower than those offered by private money lenders, who flourish in these areas.
The Hanumayamma Analytics platform offers the best cost reduction for lowering fertilizer expenses for small farmers. Hanumayamma's analytic predictions and heuristic linear programming modules give practical guidance to small farmers on how to use fertilizer efficiently and cut down agricultural costs- seeds, equipment, fertilizer, human labor- leading to a more prosperous and abundant world with wealthy small farmers.
Agriculture is a sector that involves many uncertainties, risks, losses, and hard work. However, the benefits of agriculture-- especially for small farmers-- are very low or, sometimes, non-existent. It is understandable that many small farms, in the U.S.A and other countries, have vanished. There are two types of risks that farmers face: internal and external farm risks. Internal farm risks-- risks related to soil, fertilizers, crop growth stages, and personal/family issues-- are manageable by farmers. External risks, on the other hand, are outside a farmer’s control; examples of such external farm risks are commodity price fluctuations, macroeconomic conditions, unexpected effects of real-time price models, trade conflicts, sudden shifts in people's preferences & opinion of a food commodity, and global climate change.
Risks from how climate change & weather events affect agriculture are important for every industry and service sector of the economy. One could say that weather events leave their mark on macroeconomic indicators and Gross Domestic Product (GDP). For example, the direct impact of weather events can be seen in construction, retail, agriculture, oil & gas, entertainment, travel, and public health. The weather influences the project cost estimation, schedule, delivery, and productivity of business-related activities of these industries. Adaptive analytics is crucial for understanding and planning for how weather events affect agriculture.
The combination of commodity models and weather model events with the addition of ensemble machine learning models are the best signal gatherers; they can forecast crop production and give information about the history of weather events to reduce small farmer risk.
The Hanumayamma Analytics platform processes climate data, storm data, and weather data constantly to refresh agriculture risk models that can help small scale farmers understand the impacts of climate change on agriculture outputs. Our aim is clear and simple: every farmer should have access to artificial intelligence enhanced insights, and the knowledge they can offer. This is a tool that everyone across the globe should have!
