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Smart Sensing: bridging clinical accuracy and daily life through reliable technology

In parallel, the smart sensing department finalized the technical validation of the physical monitoring kit. This process involved the functional testing of the TOLIFE smart sensors to assess their performance compared to reference instruments, ensuring they were accurate, affordable, and user-friendly for patients’ daily lives.

This integrated hardware suite includes:

• a smart mattress cover, equipped with sensors that track heart and breathing rates during sleep, as well as its quality;
• smart shoes, that analyze walking patterns and posture, providing vital data on daily mobility and physical activity;
• a smartwatch and smartphone, that working in tandem, monitor heart rate and heart rate variability, while acting as the core engine that drives the system’s data flow;
• a mini-spirometer, a portable device that, combined with a custom-developed app, allows patients to easily measure their lung function and blood oxygen levels from the comfort of home.

The latest field results confirm that the TOLIFE kit is an exceptionally powerful and reliable tool, maintaining a high level of stability with an average data loss rate of only 2%. This consistent performance ensures that doctors receive a continuous stream of dependable data, capturing both physiological signals and daily habits to provide a comprehensive picture of how COPD affects a patient’s life. Beyond its technical reliability, the prototypes have also been rigorously tested to meet safety and electromagnetic compatibility standards, fully validating their suitability for home use.

From Cloud to Clinic: optimizing the collection platform and software interfaces for real-world impact

The development of the digital gateways connecting these tools reached its peak within the collection platform andsoftware interfaces work stream. During this crucial phase, the project finalized the “invisible engine” of TOLIFE project: a sophisticated cloud infrastructure designed to gather and organize massive amounts of data from wearable sensors, health questionnaires and medical reports.

The primary achievement of this period was the completion of two essential software tools, each designed to meet the specific needs of the project’s stakeholder. These tools are the result of an innovative fusion between cutting-edge Artificial Intelligence and advanced monitoring sensors to harvest authentic clinical insights from patients’ daily routines.

The first component is the Patient Management Tool (PMT), a sophisticated dashboard that aggregates real-time data directly from patients’ wearable devices to provide clinicians with a clear, comprehensive overview of exacerbation risksand quality life metrics. This web application allows doctors to monitor multiple patients simultaneously, intervene promptly thanks to risk alerts, and customize treatment plans based on individual needs.

During this stage, the PMT evolved from a simple display into a comprehensive management system: it now handles the logistics of the clinical trial, such as recruiting participants and setting up their devices.

A key priority was also enhancing sensor data quality control through new automated workflows designed to handle sensor malfunctions, questionnaire discrepancies, and visit scheduling. This ensures that the medical team always works with high-quality, reliable information without having to manually check every single data point.

The second tool is the Disease Information Tool (DIT), a software solution co-created through workshops with patients and Patient Advisory Group to ensure it meets real-world needs. Moving beyond simple data visualization, this user-friendly application empowers patients and caregivers by providing real-time risk alerts, personalized lifestyle indications, and treatment information tailored to the user’s real-time health status.

Another innovative update in this period was the integration of environmental data. Since the health of people living with COPD is deeply affected by their surroundings, the system now automatically connects to air quality and weatherservices in the three pilot sites (Spain, Italy and Germany) where the study is taking place. By linking environmental conditions to the patient’s own health data, doctors can gain a much clearer picture of how external factors impact their patients’ daily lives.

Behind the scenes, the team has worked tirelessly to ensure the system is secure, private, and easy to use. Every component has been rigorously tested to make sure the software is stable and the data is protected. To make the transition as smooth as possible for the medical staff, the team also developed comprehensive online user manuals, reducing the need for lengthy technical training.

Observing to Prevent: building the evidence base for personalized COPD treatment

Evidence for these technological advancements comes from the ongoing Clinical Studies.

A key milestone of this stage was the implementation of Clinical Study A, designed to monitor COPD patients through their daily lives by recording data from wearable devices sensors. These information support the development of AI tools described above for the early detection of severe exacerbations and the assessment of health outcomes.

The clinical study was conducted at three sites in different EU countries – Italy (UNIPI), Spain (IMIM) and Germany(PRI) – and planned to enroll 150 patients to be followed for 12 months.

The clinical study has successfully enrolled 92 participants, reaching 61% of the total target through activities at the Spanish and German sites. Although bureaucratic challenges have delayed the Italian launch until August 2025 and impacted the timeline for the subsequent study (Clinical Study B), recruitment goals are being met by intensifying efforts at the most active locations. Furthermore, a new partnership with Thorax Research Foundation has been established to accelerate upcoming phases.

In terms of technical development, a sufficient volume of high-quality data has already been successfully collected, allowing the development of AI-based predicting models capable of forecasting patient deterioration.

Besides, to ensure the data accuracy, an automated monitoring system has been implemented to function as a “digital supervisor”, systematically checking all collected information for potential inconsistencies. Monthly reports are generated to identify and rectify any errors.

As the project enters its third and final window, preparations for Clinical Study B are already underway. While Study A is observational in nature, Study B focuses on intervention and action. This upcoming phase is designed to test the effectiveness of the TOLIFE intervention, specifically evaluating whether personalized treatments based on sensor data can effectively reduce the risk of exacerbations. The new partner, Thorax Research Foundation, has been integrated into the team to support recruitment efforts and facilitate the application of lessons learned from the initial study.

26 January 2026

Walking is far more than a physical act; it is a complex reflection of our respiratory, neurological, and psychological health.

While standard clinical evaluations remain essential for diagnosis, there is a growing recognition of the need to complement these “point-in-time” assessments with long-term data collected in a patient’s natural environment. In this context, gait speed (GS) has emerged as the “sixth vital sign” offering a sensitive and reliable metric for predicting health outcomes across different conditions, such as cardiovascular, respiratory, neurodegenerative and psychiatric diseases.

In patients with chronic obstructive pulmonary disease (COPD), mobility loss is a primary prognostic factor for mortality and increased healthcare costs. However, despite its significance, real-world mobility tracking is not yet a standard clinical practice. Bridging this gap through non-invasive sensors and AI-driven predictive algorithms is crucial for improving disease management, identifying exacerbations early and personalizing long-term patient care.

TOLIFE solution: a multimodal approach to personalized care

To address the clinical imperative for a longitudinal mobility surveillance in COPD patients, the European Union-funded TOLIFE project has developed a pioneering methodology designed to bring diagnostic precision into the patient’s home. By using Artificial Intelligence and discreet sensors that monitor heath conditions during daily life activities, TOLIFE aim to predict flare-ups and to provide personalized interventions, ultimately improving the quality of life for COPD patients.

As a central part of this mission, TOLIFE has conducted a scientific study to develop a novel method for estimating gait speed from a heterogeneous set of wearable devices. The research investigated how smartphones, smartwatches and smart shoes can work both independently and in concert to provide accurate mobility insights: by exploring these technological synergies, TOLIFE developed a flexible framework that adapts to each patient’s lifestyle, allowing them to use whichever combination of devices they find most comfortable or accessible.

From wrist to feet: How AI decodes every step

This specific study focused on three daily items from the TOLIFE kit:

• a smartphone;
• a smartwatch;
• a pair of smart shoes.

The smartwatch and the smartphone use internal motion sensors to track the patient’s balance and movement patterns. The smart shoes have an electronic unit integrated into the heel area of the insole and three pressure sensors that monitor how the foot strikes the ground. TOLIFE team built two specialized Android apps to gather information from the phone and watch sensors. The smartphone app also functioned as a hub to receive data from the smart shoes via Bluetooth. By bringing together data from the wrist, the pocket and the feet, the system creates a complete picture of a patient’s mobility.

To verify the model’s accuracy, the data sensors were compared with that of a high-precision professional system, called Xsens Awinda. This system makes use of 17 sensors placed across the body to measure movement speed with extreme precision. The data collected by this reference system have been used as benchmark to train our model to correctlymeasure walking speed.

The research involved 20 young healthy volunteers who wore the reference system and the experimental devices, with the smartwatch on the left wrist and the smartphone in the front pocket.

The subjects had to perform a modified version of the of the Six-Minute-Walking-Test (6MWT), which required them to walk along a 10-meter flat path, allowing about 50 cm for each turn. Each person completed the test three times at different speeds: slow, medium and fast; this allowed to analyze how the sensors recorded movement across a wide range of walking paces while keeping the devices in the same position.

To find the most accurate setup for tracking movement in daily life, the devices were employed one by one, collectively or in combination. Therefore, 7 different systems were tested:

• phone;
• watch;
• shoes;
• phone + watch;
• phone + shoes;
• watch + shoes;
• all the devices.

The study evaluated the accuracy of every configuration of wearable sensors, identifying the key variables that most heavily influence the AI-driven predictive models.

Therefore, 7 distinct machine learning models were created, one for every possible combination of these devices: after a pre-processing phase of the raw data, the sensor signals were divided into small 5-second segments; from these segments, several key statistical values (features) were extracted (148 for phone and watch, and 222 for shoes), so a smart selection process was applied to isolate only the 10 most important characteristics for each device combination. After defining the models’ mathematical formulas, the algorithm was validated through the “Leave-One-Subject-Out”approach: the model was trained on data from all subjects, excluding one individual used as a final test case; this procedure was repeated for each participant to ensure the system is robust and functions correctly with any new user.

Which wearable combination truly wins?

The findings demonstrated that the models achieved a high level of accuracy in estimating walking speed.

The standalone contenders

At first glance, the smartphone appears to be the champion of accuracy. Because it’s typically carried in a pocket, close to the body’s center of mass, it provides very precise speed readings. However, during the tests, the phone was kept in a fixed controlled position. In real life, phones bounce around or sit in bags, which might make these results hard to replicate.

In contrast, the smartwatch proved to be a more reliable “all-rounder”. While it may be slightly less accurate than the phone on its own, its consistent presence on the wrist ensures more dependable data in real-world conditions. Furthermore, the smartwatch captures additional physiological metrics, such as heart rate (HR), which could potentially further enhance prediction accuracy.

The winning combination

The real breakthrough occurred through the pairing of multiple devices. It was found that the ultimate “effective formula” for tracking walking is the smartwatch paired with smart shoes. This duo achieved the highest level of accuracy, outperforming even the most stable smartphone readings. It appears that data from the wrist and the feet complement each other, effectively filling the information gaps that a single device might miss.

The surprise: less is more

Interestingly, evidence suggests that using all devices at once actually worsened the results. This might seem counterintuitive, but it is primarily due to “data noise”. When a model receives too much overlapping information from various sensors, it struggles to distinguish between relevant and redundant data, which in turn reduces its predictive power.

Future perspectives: A new era of flexible health monitoring

This research marks a significant milestone in wearable technology, showcasing how a strategic assembly of devices can redefine health monitoring in real-world settings. By moving beyond the reliance on a single sensor, TOLIFE system utilizes an intelligent algorithm that adapts to any combination of these three tools. This creates a robust failsafe: whether the devices work together as a powerhouse ensemble for maximum precision or function independently, the flow of health data remains uninterrupted and accurate, even if one device runs out of power or is temporarily removed.

The true value of this multi-device approach lies in its patient-centered design. Giving individuals the flexibility to choose the most suitable device for their daily activities significantly improves long-term compliance. For those managing chronic conditions like COPD, this means moving from reactive to proactive care. By detecting subtle, day-to-day changes in mobility, clinicians can personalize treatment plans and intervene early, potentially preventing complications and enhancing overall quality of life.

Ultimately, this framework offers a new standard for remote healthcare. While particularly effective for respiratoryhealth, its implications extend to any condition where mobility is a key indicator, such as Parkinson’s disease, heart failure or mental disorders. This study proves that the future of medical monitoring lies in technology that is as dynamic and adaptable as the patients who use it.

Interested to know more about this research? Read the full scientific article here: https://www.mdpi.com/1424-8220/24/10/3205

9 October 2025

The TOLIFE consortium gathered in Bologna for the third in-person General Assembly since the project’s beginning. The meeting, hosted by Tinexta Innovation Hub | beWarrant, brought together all partners to review progress, discuss ongoing activities, and plan the next steps for the coming months.

The session was opened by Alessandro Tognetti, project coordinator, who provided an overview of the work accomplished so far and outlined the roadmap ahead. He also welcomed the representatives of Thorax Foundation, the newest member of the consortium, who introduced themselves and their contribution to the project.

Throughout the meeting, partners presented updates from their respective work packages, covering a wide range of topics from the clinical aspects to the development of smart sensors and the user interface. The discussions highlighted the collaborative spirit of the consortium and the shared commitment to advancing innovative digital solutions for respiratory health.

Both patients and members of the Advisory Board expressed their satisfaction with the project’s progress, emphasizing their appreciation for being actively involved in the work and for the continuous and meaningful engagement ensured by the consortium.

The General Assembly also served as an important step in preparation for the upcoming Review Meeting, which will take place in the following weeks.

29 November 2024

The TOLIFE project, now two years in, has published its second press release, highlighting the successful development of non-invasive devices and the commencement of Clinical Study A. This pivotal phase marks a significant step toward revolutionizing COPD care through AI-driven, personalized solutions.

Read the full press release to discover how these innovations are shaping the future of chronic disease management.

9 July 2024

The TOLIFE project, aimed at revolutionizing the management of Chronic Obstructive Pulmonary Disease (COPD), has officially commenced its Clinical Study A. This milestone marks a significant step forward in the project’s mission to enhance the quality of life for COPD patients through personalized treatment and advanced technological solutions.

The Role of AI in COPD Management

TOLIFE leverages artificial intelligence (AI) to process daily life patient data captured by unobtrusive sensors. This AI-driven approach is designed to optimize personalized treatment, assess health outcomes, and ultimately improve the quality of life for COPD patients. By predicting and mitigating exacerbations, the TOLIFE platform aims to reduce mortality, enhance health-related quality of life, and lower healthcare costs.

Clinical Study A: A Key Component of TOLIFE

Clinical Study A is a crucial element of the TOLIFE project. The recruitment started in June 2024 and each participant will be monitored for 12 months. This study will focus on registering exacerbations and conducting periodic clinical examinations to provide essential clinical references for the AI tools being developed. The data collected from this study will be instrumental in:

1. Developing a model to detect severe COPD exacerbations at an early stage.
2. Creating a model to assess the impact of COPD and its comorbidities on patients’ health status and quality of life.

Progress and Next Steps

The Clinical Study A has successfully begun, with all necessary smart sensor kits delivered to Germany and Spain. Patients have started participating in the trial, and the platform is already collecting initial data for analysis. This early data collection is a promising start, indicating that the TOLIFE platform is on track to deliver valuable insights and improvements in COPD management.

By integrating advanced AI technologies with patient care, TOLIFE aspires to set a new standard in managing chronic diseases like COPD. The project’s commitment to enhancing patient outcomes through innovative solutions continues to drive its efforts forward.

Stay tuned for more updates on the progress and findings of Clinical Study A and other initiatives under the TOLIFE project.