Transforming Elderly Care Through AI

Ingesan

Ensuring the best care for individuals who require home assistance—such as those with dependency needs—is becoming an increasing priority in our society. The social and healthcare division of Ingesan (part of the OHLA Group) understands this well. The demand for personalized attention in home care has grown significantly in recent years, prompting them to seek new ways to enhance the quality of their services.

Ingesan had already seen how technology could improve their care model. This was evident with VERA, the first digital model for remote psychosocial care recognized by a public administration in Spain. VERA delivers psychological, social, and physical support sessions to the homes of individuals in situations of dependency or vulnerability through their television, helping reduce feelings of loneliness and improve their quality of life through meaningful companionship.

Building on this success, they decided to take it a step further by implementing a home monitoring system based on IoT devices, designed to collect real-time data from users. While the data was already being captured and interpreted by healthcare professionals to monitor different aspects of users’ daily routines, relying solely on staff created a significant workload—both in terms of time and cognitive demand—and hindered the scalability of the solution.

Achieving scalability and expanding support to more users without creating bottlenecks was critical to the success of their technology-driven approach. That’s why Ingesan needed a partner with deep technical expertise and the R&D capacity to create a disruptive solution capable of addressing this challenge.

About the client:

Ingesan firmly believed that continuous, personalized monitoring of dependent individuals—designed to anticipate their needs and complement in-person visits and the periodic sessions provided through VERA—would enhance their well-being and ensure more attentive care. With this goal in mind, they had implemented a solution from Canadian start-up Aerial Technologies a year and a half earlier. This patented monitoring system, using non-intrusive devices installed in the home, detects presence and movement by interpreting distortions in the electromagnetic field generated by a standard Wi-Fi network.

The system was already in operation with a limited number of users, but Ingesan now faced two major challenges: scaling up to serve more people, and turning the collected data into actionable insights.

Without a system capable of extracting meaningful information from the data, it would be very difficult for Ingesan’s staff to derive real value from it. Since Ingesan’s core focus is on delivering professional care services, they needed external support to develop this new solution. But this wasn’t a decision to take lightly—they were looking for a team aligned with their innovation mindset, willing to work side by side, and capable of understanding both the business needs and the research-driven nature of the project.

The success of this initiative was strategic. It wouldn’t just benefit the users—it would also reinforce other efforts like VERA, which had already proven highly effective. Successfully tackling this use case was essential to strengthening their commitment to integrating technology with social and healthcare services. It was a big responsibility, and finding the right partner became a top priority.

“We were looking for a tech partner we could trust to develop this project. We’re dealing with people, with a sensitive area, and therefore with sensitive data, so we needed quality assurance. When we learned about Sciling’s experience and their team of PhDs and specialized engineers, we knew the project would be in good hands”, said David del Río, Head of Business Innovation at Ingesan.

Visión estratégica:

The collaboration between Ingesan and Sciling resulted in an intelligent, non-intrusive monitoring system that enables social and healthcare professionals to accurately track each user’s habits in real time and receive prioritized alerts based on severity when significant changes in routines are detected.

Through a web application, professionals can access each user’s history, view trends and details over specific time periods—such as sleep duration, wakefulness, and other key indicators of daily activity. This smart analysis complements phone, video, or in-person follow-ups, allowing them to support more people with greater precision and efficiency.

The system quickly proved its value. “One of the most revealing cases was that of a user whose sleep pattern changed drastically: she usually slept seven hours, but one night she barely managed three, and her rest was highly fragmented. This sudden change caught the attention of the Ingesan team, who contacted her caregivers to schedule a professional visit. They found that her brother had been admitted to the hospital in an emergency, which triggered an anxiety crisis and disrupted her sleep. Thanks to early detection, they were able to step in quickly and help her recover her well-being within a few days. Without the system, the issue might have gone unnoticed, with a real impact on her quality of life,”explains Ion Marqués, Machine Learning Researcher at Sciling.

The benefits go even further. The system has also proven its ability to assess whether the activities prescribed by the care team truly align with the users’ needs, preferences, and interests—allowing for adjustments to deliver more personalized support.

Currently, the system is being used with more than 20 individuals and continues to expand, becoming a key tool for early intervention and personalized care. With this innovative system, Ingesan has become a pioneer in developing the first Internet of Behaviours (IoB) solution for social and healthcare assistance in Spain, laying the groundwork for a more efficient, scalable care model focused on real well-being.

Following this success, both Ingesan and Sciling are already exploring new initiatives and potential collaborations to drive further positive change in the social and healthcare sector.

From the very beginning, the Sciling team understood that this project wasn’t just about technology. It was about creating real impact in people’s lives.

Following an initial meeting to understand Ingesan’s needs, the team applied a proprietary methodology grounded in years of research experience. Drawing on their deep expertise in data analysis and predictive analytics, they assessed the value of the data collected by the devices and how it could be translated into meaningful insights to support the work of social and healthcare professionals.

They then integrated this data with historical records, key indicators, and the existing scales used to assess the relevance or severity of changes in user routines. As a result, they were able to provide an intuitive tool to detect significant changes in users’ day-to-day activities.

One of the biggest challenges was ensuring that the alerts were both accurate and actionable. As Armando Gomis, Machine Learning Engineer at Sciling, explains:

“We had to continuously optimize the detection models so that the alerts would faithfully reflect changes in user routines and align with their specific needs. Thanks to this ongoing effort, the system has steadily improved in accuracy and adaptability, becoming a truly valuable and effective resource for the Ingesan team.”

Beyond the technology itself, this project has shown that AI can be successfully integrated into the social and healthcare sector without compromising the trust of professionals or the safety of users.

“We know that selling innovation means selling risk. If there’s no risk, there’s no innovation. Selling AI generally means a higher level of innovation risk—especially in the social and healthcare space. But it also brings greater opportunity. That’s why you need to work hand in hand with people who know what they’re doing,” says David del Río.