Wearable sensor system and uses thereof

Abstract

The present invention relates to a wearable sensor system in the form of a smart clothing garment for use in the non-invasive and accurate assessment of progression of disease in patients suffering from dementia and more particularly from Alzheimer΄s disease.

Description

[0001] TITLE

[0002] WEARABLE SENSOR SYSTEM AND USES THEREOF

[0003] DESCRIPTION

[0004] FIELD OF THE INVENTION

[0005] The present invention relates to wearable sensor systems, preferably in the form of smart clothing garments and the use of such wearable sensor systems in the non- invasive and precise assessment of progression of disease in patients with dementia, preferably with Alzheimer's disease.

[0006] BACKGROUND

[0007] Dementia is a term used to describe the symptoms of a large group of illnesses that cause a progressive decline in a person’s mental functioning. It is a broad term that describes symptoms such as the loss of memory, intellect, rationality, social skills, and normal emotional reactions. Mild cognitive impairment (MCI) is considered an intermediate state between normal age-related cognitive level and dementia (Chertkow et al; CMAJ; 2008:178, 1273-1285), and between 19 and 50% of patients with MCI progress are diagnosed with a form of dementia within 3 years. Currently, 10% percent of the population is over 60, this age group is more likely to suffer physical and cognitive impairments. Worldwide, by 2030, it is estimated that 74.7 million people (100 million by 2050) will progress to the most severe form of dementia, Alzheimer’s disease (WHO Dementia: A Public Health Priority.World Health Organ. 2020. Available online: https / / www.who.int / mental_health / publications / dementia_report_2012 / en / (a].) Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by progressive cognitive decline, memory loss, and impaired daily functioning. It accounts for a significant majority, approximately 60- 75%, of dementia cases worldwide (Alzheimer's Association, 2021). The global impact of AD is substantial, with an estimated 35.6 million people affected (Prince et al., 2013). Furthermore, the burden of AD is expected to rise significantly in the coming decades due to increasing life expectancy and population growth, particularly in Europe. It is projected that the number of AD cases will increase by 87% from 2010 to 2050 in Europe alone (Wimo et al., 2013). Therefore, a systematic approach is required to assist all parties, the patient, carers, medical practitioners and others, to cope with this increase.

[0008] Currently, the diagnosis of AD relies primarily on clinical evaluation and questionnaires, which are subjective and may lack precision (Gaugler et al., 2019). Moreover, these methods often do not provide the discriminating ability to accurately stage the disease in patients. On the other hand, interventional techniques, such as lumbar puncture for the detection of AD markers in cerebrospinal fluid, MRI, and amyloid-PET scan, offer increased accuracy but are invasive, time-consuming, and costly (Scheltens et al., 2016). These limitations highlight the need for non-invasive and continuous monitoring solutions that can accurately assess disease progression and optimize treatment strategies for AD.

[0009] In the past years, wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and / or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease (Moon, S. et al.:J. Neuroeng. Reliability 7, 125 (2020); Un, K. C. et al.; Sci. Rep. 11 , 4388 (2021)), through biophysical signals.

[0010] Use of wearable devices technology in the form of appropriately designed and manufactured smart clothing garments can overcome the so far faced limitations in the accurate and diagnosis and prognosis of Alzheimer's disease.

[0011] SUMMARY

[0012] The present invention relates to wearable sensor systems, preferably in the form of smart clothing garments and uses thereof in the non- invasive and precise assessment of progression of disease in patients with dementia, preferably with Alzheimer's disease.

[0013] In one aspect the present invention relates to a wearable sensor system comprising a clothing garment, one or more sensors attached, embedded or integrated to said clothing garment suitable for the collection of data from a subject wearing said wearable sensor device, a transmitter or transceiver wirelessly communicating the collected data and a battery powering the sensor or sensors and transmitter or transceiver.

[0014] In one embodiment said wearable sensor system comprises one or more sensors wherein said sensor or sensors wirelessly transmit collected data through a transmitter or transceiver.

[0015] In one embodiment said wearable sensor system comprises a clothing garment with one or more sensors attached, embedded or integrated there to, said sensor or sensors are selected from accelerometers, gyroscopes, magnetometers, GPS, pedometers, heart rate sensors, and / or pressure sensors.

[0016] In one embodiment said wearable sensor system comprises a clothing garment with one or more sensors attached, embedded or integrated there to, said sensor or sensors are fibre sensors. In one embodiment said wearable sensor system comprises a clothing garment, wherein said clothing garment is made of natural and / or synthetic fibres and / or blends thereof.

[0017] In another aspect the present disclosure relates to a method for assessing the progression of disease of a subject suffering from dementia preferably Alzheimer's disease, said method comprising the steps of

[0018] -collecting data from a subject wearing a clothing garment system using one more sensors attached, embedded or integrated to said clothing garment system,

[0019] -wirelessly transmitting said collected data through a transmitter or a transceiver to a central analysis unit,

[0020] -analyzing said collected data with the use of algorithms, said algorithms being able to identify patterns, trends, and / or correlations suitable to Alzheimer's disease or symptoms

[0021] -saving and storing said collected data to a cloud computing means

[0022] In another aspect the present disclosure relates to a method for assessing the progression of disease of a subject suffering from dementia preferably Alzheimer's disease, said method comprising the steps of

[0023] -analyzing collected data by one or more sensors attached, embedded or integrated to a clothing garment system from a subject wearing said clothing garment system and wirelessly transmitted through a transmitter or a transceiver to a central analysis unit, with the use of algorithms, said algorithms being able to identify patterns, trends, and / or correlations suitable to Alzheimer's disease or symptoms

[0024] -saving and storing said collected data to a cloud computing means

[0025] In another aspect the invention relates to a wearable sensor system for use in the assessment of progression of disease of a subject suffering from dementia, preferably Alzheimer's disease.

[0026] In another aspect the invention relates to use of said wearable sensor system for the prognosis of Alzheimer's disease of a subject.

[0027] BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 : Exemplary illustration of a clothing garment in the form of a T-shirt comprising an attached sensor wirelessly transmitting collected data. DETAILED DESCRIPTION

[0029] It is a subject of the present disclosure to provide a wearable sensor system in the form of a smart clothing garment for use in the non-invasive and accurate assessment of progression of disease in patients suffering from dementia and more particularly from Alzheimer's disease.

[0030] Wearable sensor technologies have emerged as promising tools for monitoring various aspects of health and well-being. These wearable devices, including smartwatches, fitness trackers, and smart clothing, are equipped with sensors such as accelerometers, gyroscopes, magnetometers, GPS, heart rate sensors, and pressure sensors. They enable the collection of real-time data on physiological parameters, movement patterns, and environmental factors (Ferguson et al., 2019). The integration of wearable sensors into a smart garment specifically designed for individuals with AD holds great potential for improving the accuracy of disease diagnosis, staging, and treatment optimization.

[0031] The wearable sensor system of the present disclosure aims to collect and analyze data from multiple sensors embedded within the fabric, providing a comprehensive and continuous assessment of various parameters related to AD. These parameters include physical functions, response to stimuli, stress levels, inability to coordinate movement and balance, and other symptoms associated with the disease. By correlating these data with the clinical phenotype, the smart clothing garment can accurately stage the disease and provide valuable insights into the progression of AD.

[0032] The advancement of artificial intelligence (Al) techniques, particularly machine learning and data mining, further enhances the potential of the smart garment for AD management. By leveraging Al algorithms, the collected data can be analyzed to identify patterns, trends, and correlations that may not be readily apparent to human observers. This analytical capability enables the development of predictive algorithms that can forecast disease progression and optimize treatment strategies based on individual patient profiles (Hindriks et al., 2020). The continuous monitoring facilitated by the smart clothing garment, coupled with real-time data analysis and predictive capabilities, can significantly improve the prognosis and quality of life for individuals with AD.

[0033] Furthermore, the incorporation of cloud computing technologies in the smart clothing garment system provides several advantages. Cloud computing allows for the storage, processing, and remote access of large amounts of data collected from multiple patients. It enables healthcare professionals and caregivers to access patient information securely, fostering collaboration and facilitating personalized care (Lam et al., 2019). The cloud-based infrastructure also supports the retraining of Al models in real-time, ensuring that the predictive algorithms remain accurate and adaptable to individual patient needs as the disease progresses.

[0034] The development of a smart garment for the prognosis and treatment optimization of AD represents a promising and innovative approach to address the challenges posed by this debilitating disease. By leveraging wearable sensor technologies, Al algorithms, and cloud computing, the smart garment can provide continuous and non-invasive monitoring of patients, leading to early diagnosis, accurate disease staging, and personalized treatment strategies. This technology has the potential to significantly improve the lives of individuals affected by AD and optimize healthcare resource allocation in the face of the growing global burden of dementia.

[0035] The smart clothing garment of the present disclosure, suitable for the prognosis and treatment optimization of Alzheimer's disease (AD), offers numerous advantages and utilities that can significantly impact the management of the disease.

[0036] One of the primary advantages of the smart clothing garment system is its ability to provide non- interventional and continuous monitoring of individuals with AD. Unlike traditional diagnostic methods that rely on periodic clinical evaluations, the smart garment collects data on a 24-hour basis. This continuous monitoring offers a comprehensive view of the individual's physical functions, movement patterns, stress levels, and other AD-related symptoms. The continuous data collection enhances the accuracy of disease staging and provides valuable insights into the progression of AD over time. Moreover, the non-invasive nature of the smart garment eliminates the need for invasive procedures, such as lumbar puncture or imaging scans, which are often time-consuming, costly, and burdensome for patients. By leveraging wearable sensor technologies, the smart garment allows for unobtrusive and convenient monitoring, improving patient comfort and compliance.

[0037] Early diagnosis of AD is crucial for initiating timely interventions and improving the quality of life for individuals affected by the disease. The smart clothing garment system facilitates early detection by continuously monitoring various parameters associated with AD symptoms and disease progression. By analyzing the collected data using Al algorithms, the system can identify subtle changes in movement patterns, physiological responses, and other AD-related indicators, enabling early diagnosis and intervention. Early diagnosis and prevention strategies can help individuals with AD and their caregivers better understand the disease, access appropriate care and support services, and make informed decisions regarding treatment and lifestyle modifications. The smart clothing garment system holds the potential to significantly impact the prognosis and outcomes of individuals with AD by enabling early interventions that may slow down disease progression and improve overall well-being.

[0038] Each individual with AD presents a unique clinical phenotype and responds differently to treatments. The smart clothing garment system, coupled with Al-based data analysis, allows for personalized treatment optimization based on individual patient profiles. By correlating the collected sensor data with the clinical phenotype and disease progression, the system can generate predictive algorithms that tailor treatment strategies to the specific needs of each patient. The personalized treatment optimization facilitated by the smart garment system can improve the effectiveness of interventions, reduce adverse effects, and enhance overall treatment outcomes. Healthcare providers can utilize the system's insights to make informed decisions about medication adjustments, lifestyle modifications, and therapy interventions, ultimately improving the quality of care and quality of life for individuals with AD.

[0039] The smart clothing garment system provides real-time prognosis and disease monitoring capabilities, allowing for dynamic assessment of AD progression. By continuously analyzing the collected data using Al algorithms, the system can predict and track disease stages in real-time. This real-time monitoring enables healthcare providers to make timely and informed decisions regarding treatment adjustments, care planning, and resource allocation. The ability to monitor disease progression continuously and in real-time is particularly valuable for individuals with AD, as the disease is characterized by fluctuations in symptoms and progression rates. The smart garment system's realtime prognostic capabilities empower healthcare providers and caregivers with the tools to adapt interventions and support strategies as the disease evolves, leading to better outcomes and improved quality of life for individuals with AD.

[0040] The development of a smart clothing garment for AD management represents a significant innovation in the field of healthcare technology. By integrating wearable sensor technologies, Al algorithms, and cloud computing infrastructure, the smart garment system pioneers a new approach to non-invasive, continuous monitoring, and personalized treatment optimization. The broader vision behind this project extends beyond the individual benefits to the potential for transforming dementia care on a larger scale. The smart garment system, with its ability to collect and analyze large amounts of patient data, can contribute to the development of comprehensive AD databases and research repositories. These data repositories can serve as valuable resources for researchers, clinicians, and policymakers, leading to advancements in understanding AD pathophysiology, improving diagnostic accuracy, and identifying novel therapeutic targets.

[0041] The smart garment clothing system's integration with cloud computing enables remote access to patient data, facilitating telemedicine and telemonitoring initiatives. This connectivity allows healthcare providers to remotely monitor patients, provide timely interventions, and support caregivers in real-time. The system's potential for remote monitoring and collaborative care delivery holds great promise for improving healthcare access and delivery, especially in remote or underserved areas.

[0042] Therefore, in one aspect the present invention relates to a wearable sensor system comprising a clothing garment, one or more sensors attached, embedded or integrated to said clothing garment suitable for the collection of data from a subject wearing said wearable sensor device, a transmitter or transceiver wirelessly communicating the collected data and a battery powering the sensor or sensors and transmitter or transceiver.

[0043] In one embodiment said wearable sensor system comprises a clothing garment with one or more sensors attached, embedded or integrated there to, wherein said sensor or sensors are selected from accelerometers, gyroscopes, magnetometers, GPS, pedometers, heart rate sensors, temperature sensors, and / or pressure sensors.

[0044] In one embodiment said wearable sensor system comprises a clothing garment, wherein said clothing garment is made of natural and / or synthetic fibres and / or blends thereof.

[0045] In one embodiment said wearable sensor system comprises a clothing garment, wherein said clothing garment is a shirt, a T-shirt, a dress, a sleeping gown, a pajama, a sweatpant.

[0046] In another aspect the present disclosure relates to a method for assessing the progression of disease of a subject suffering from dementia preferably Alzheimer's disease, said method comprising the steps of

[0047] -collecting data from a subject wearing a clothing garment system using one more sensors attached, embedded or integrated to said clothing garment system,

[0048] -wirelessly transmitting said collected data through a transmitter or a transceiver to a central analysis unit,

[0049] -analyzing said collected data with the use of algorithms, said algorithms being able to identify patterns, trends, and / or correlations suitable to Alzheimer's disease or symptoms

[0050] -saving and storing said collected data to a cloud computing means

[0051] In another aspect the present disclosure relates to a method for assessing the progression of disease of a subject suffering from dementia preferably Alzheimer's disease, said method comprising the steps of

[0052] -analyzing collected data by one or more sensors attached, embedded or integrated to a clothing garment from a subject wearing said clothing garment system and wirelessly transmitted through a transmitter or a transceiver to a central analysis unit, with the use of algorithms, said algorithms being able to identify patterns, trends, and / or correlations suitable to Alzheimer's disease or symptoms

[0053] -saving and storing said collected data to a cloud computing means

[0054] In another aspect the invention relates to a wearable device for use in the assessment of progression of disease of a subject suffering from dementia, preferably Alzheimer's disease.

[0055] In another aspect the invention relates to use of said wearable sensor device for the prognosis of disease of a subject suffering from dementia, preferably Alzheimer's disease. Definitions

[0056] Unless otherwise defined, scientific and technical terms used herein have the meanings that are commonly understood by those of ordinary skill in the art. In the event of any latent ambiguity, definitions provided herein take precedent over any dictionary or extrinsic definition.

[0057] The term “transmitter” refers to an electronic component that generates a radio frequency (RF) current or radio waves. These waves are used in communication systems to transfer data like audio, video, etc.

[0058] The term “transceiver” refers to an electronic device able to wirelessly transmit, as well as receive, different signals.

[0059] The terms “smart clothing garment”, “smart clothing garment system”, “clothing garment” and “clothing garment system” are used in the context of the present disclosure interchangably and refer to any kind of clothing garment or accessory suitable to be worn by a human, comprising a sensor suitable for use in the collection of any biological or biometric data.

[0060] The term “about” or “approximately” means the mentioned value + / -10%, for example about 10 shall mean 9 to 11 .

[0061] Unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. The use of “or” means “and / or” unless stated otherwise. The use of the term “including,” as well as other forms, such as “includes” and “included,” is not limiting.

[0062] While the present invention has been described with reference to the specific embodiments thereof, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the true spirit and scope of the invention using this disclosure as a guide. for construction of a smart and the su and creation of smart

[0063] Selection of clothing garment

[0064] The clothing garment to be used for the construction of the smart clothing garment system of the present invention can be any suitable clothing garment that can be easily worn to a patient with AD by a carer, and at the same time needs to provide the necessary surface whereby an appropriate sensor can be attached, embedded or integrated, so that data for the subsequent assessment are captured. For example such a clothing garment can be a shirt, T- shirt, dress, sleeping gown, trousers, pyjamas or any other clothing garment of the like. Additionally, for such a clothing garment it is essential that appropriate textiles and / or fabric materials are used e.g. cotton, polyester, blends thereof), such that provide endurance to continuous use and at the same time are well tolerated by the patient and do not affect the hygiene status of the patient. Figure 1 shows an exemplary clothing garment in the form of a T-shirt comprising an attached sensor wirelessly transmitting collected data from a subject wearing said T- shirt.

[0065] Selection and Integration of Wearable Sensors

[0066] The first step in developing the smart clothing garment of the invention is the careful selection and integration of appropriate wearable sensors. The sensors should be capable of capturing relevant data related to AD symptoms and disease progression. Sensors suitable for use in the production of the smart clothing garment of the invention include accelerometers, gyroscopes, magnetometers, GPS, pedometers, heart rate sensors, temperature sensors, and pressure sensors (Ferguson et al., 2015). These sensors enable the measurement of various parameters such as movement patterns, physiological responses, and environmental factors that are crucial for assessing AD symptoms and progression.

[0067] The selection of sensors considers factors such as accuracy, reliability, power consumption, and the ability to integrate seamlessly into the fabric of the smart garment. Advances in wearable sensor technology have resulted in miniaturized and energy-efficient sensors that can be easily integrated into clothing, ensuring user comfort and convenience (Ferguson et al., 2019). The integration process involves embedding the sensors within the fabric or strategically placing them in specific locations on the garment to ensure optimal data collection.

[0068] Data Collection and Transmission

[0069] Once the wearable sensors are integrated into the smart clothing garment, they begin collecting data from the wearer in real-time. The data collection process involves continuously monitoring various parameters such as physical functions, response to stimuli, stress levels, movement coordination, and other AD-related symptoms. The collected data is then transmitted wirelessly to a centralized system for further analysis. To ensure accurate and reliable data transmission, appropriate wireless communication protocols such as Bluetooth or Wi-Fi are utilized. These protocols enable seamless and secure data transfer between the smart garment and the centralized system. The data transmission should be efficient and capable of handling a high volume of data collected from multiple sensors in real-time.

[0070] Data Analysis and Al Techniques for the assessment of AD progrssion

[0071] The collected data is subjected to rigorous analysis using Al techniques to identify patterns, trends, and correlations that may provide insights into AD progression and symptomatology. Machine learning algorithms, specifically supervised and unsupervised learning techniques, are commonly employed to analyze the data. Supervised learning algorithms, such as support vector machines (SVM) and random forests, are trained using labeled data to classify and predict disease progression stages. Unsupervised learning techniques, such as clustering and anomaly detection, help identify hidden patterns and detect deviations from normal behavior.

[0072] The analysis of the collected data involves feature extraction, where relevant features or parameters are identified and quantified from the sensor data. These features may include gait characteristics, movement patterns, heart rate variability, sleep quality, and environmental factors. Feature selection techniques, such as principal component analysis (PCA) or recursive feature elimination (RFE), may be employed to reduce the dimensionality of the data and improve computational efficiency.

[0073] The Al algorithms are trained using labeled data from individuals with varying stages of AD. The labeled data includes clinical phenotype information, such as cognitive assessments, functional scales, and disease staging based on established criteria. The supervised learning algorithms learn from the labeled data to establish correlations between the sensor data and disease progression, enabling accurate staging and prediction of AD.

[0074] Retraining and Real-time Predictive Algorithms

[0075] To ensure the predictive algorithms remain accurate and adaptable to individual patient needs, continuous retraining is performed using real-time data. As new data becomes available, the Al models are updated and retrained, incorporating the latest information to improve prediction accuracy. Real-time retraining enables the smart garment system to adapt to changes in the disease trajectory, individual variations, and response to treatment. The retraining process involves the updating of model parameters, revisiting feature selection, and incorporating new labeled data into the training dataset. This iterative process ensures that the predictive algorithms remain up-to-date and can accurately predict disease progression and optimize treatment strategies for individual patients.

[0076] Cloud Computing and Data Storage

[0077] The integration of cloud computing technologies plays a crucial role in the smart clothing garment system of the present invention. The collected sensor data are securely transmitted and stored in the cloud, enabling centralized access and storage of large amounts of patient information. Cloud computing platforms offer scalable storage capacity, efficient data processing capabilities, and remote accessibility, allowing healthcare professionals and caregivers to access patient data anytime and from anywhere.

[0078] The cloud-based infrastructure also facilitates collaborative efforts between healthcare providers, researchers, and caregivers. It allows for secure sharing of data, knowledge exchange, and remote monitoring of patients, leading to more personalized and efficient care delivery. Furthermore, the cloud-based system supports the retraining of Al models using real-time data. By leveraging cloud computing resources, the computational demands for data analysis and model retraining can be efficiently managed, ensuring the system's responsiveness and accuracy.

[0079] ] I

Claims

CLAIMS1. A wearable sensor system comprising a clothing garment, one or more sensors attached, embedded or integrated to said clothing garment suitable for the collection of data from a subject wearing said wearable sensor device, a microcontroller with a transmitter or transceiver wirelessly communicating the collected data and a battery powering the sensor or sensors and transmitter or transceiver.

2. The wearable sensor system according to claim 1 , wherein said sensor or sensors are selected from accelerometers, gyroscopes, magnetometers, GPS, pedometers, heart rate sensors, temperature sensors, and / or pressure sensors.

3. The wearable sensor system according to any one of claims 1 to 2, wherein said clothing garment is made of natural and / or synthetic fibres and / or blends thereof.

4. A method for assessing the progression of disease of a subject suffering from dementia preferably Alzheimer's disease, said method comprising the steps of-analyzing collected data by one or more sensors attached, embedded or integrated to a clothing garment from a subject wearing said clothing garment, and wirelessly transmitted through a transmitter or a transceiver to a central analysis unit, with the use of algorithms, said algorithms being able to identify patterns, trends, and / or correlations suitable to Alzheimer's disease or symptoms-saving and storing said collected data to a cloud computing means5. A wearable sensor system for use in the assessment of progression of disease of a subject suffering from dementia, preferably Alzheimer's disease.

6. A wearable sensor system according to claim 5 for use in the prognosis of Alzheimer's disease of a subject.

7. Use of the wearable sensor system of any one of claims 1 to 3 in the assessment of progression of disease of a subject suffering from dementia, preferably Alzheimer's disease and / or in the prognosis of disease of a subject

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