Description:FIELD OF THE INVENTION
This invention relates to IoT based sleep quality prediction system for cancer patients.
BACKGROUND OF THE INVENTION
Sleep is an important biological activity that rejuvenates and regenerates cells in human body. There are evidences that there are increased cases of sleep issues in cancer patients in India. The invention intends to develop an IoT based sleep quality prediction system for cancer patients by utilizing a wearable device, edge vision device, cloud connectivity, machine learning, and mobile/ web application. The wearable device is a wrist band equipped with PPG sensor, accelerometer, gyroscope, temperature sensors, and body position sensor that provide comprehensive data on sleep stages, body movements, as well as body temperature. Further a rechargeable battery, storage unit, and a Wi-Fi unit is attached for internet connectivity and portable access of the smart wrist worn band. There are evidences that cancer patients experience more sleep issues as compare to the general population [1]. The sleep quality and patterns in healthy individuals and cancer patients undergoing treatment are contrasted here. It is necessary to recognise the unique sleep patterns that might be altered by effect of disease itself as well as by its treatments, like as radiation and chemotherapy, surgery, hormone therapy etc.
Factors affecting sleep quality in cancer patients: Cancer patients underwent intensive treatments experience different symptom trajectories at different stages and cycles of treatments. Cancer- related fatigue, pain, anxiety, depression, hopelessness, leg twitching, state of confusion, long pauses between breathing, nausea, hot flushes, stress, bad dreams, and impact of medications are the key factors that impact the quality of sleep of cancer patients as compare to the general population.
Need for the invention: Researches shows that there are potential psychologic, and behavioural consequences of sleep issues like insomnia in cancer patients among which cancer- related fatigue is most common which is far different from common fatigue experienced by general population. Secondly, cognitive impairment, and vasomotor symptoms are also frequently observed in cancer patients.
Below table represents the difference between the sleep quality components of cancer patients in comparison with general population[2, 3]:
Sleep components Healthy population Cancer population
Sleep latency Usually fall asleep within 30 minutes Often takes longer to fall asleep due to pain, stress, fear, and depression.
Sleep duration Typically, 7-9 hours per night Often reduced, can be less than 7 hours.
Sleep efficiency High, around 80-90% Very low, due to reduced quality sleep hours.
Daytime dysfunction Minimal unless deprived Increased daytime sleepiness and low enthusiasm.
Sleep disturbance Less Significantly more, due to pain, and frequent awakening because of urination at night.
Overall sleep quality Generally good Poor, influenced by various factors described above.

Fig 1(a, b) explains the change in sleep quality in cancer patients before and after underwent at-least one treatment cycle. Zero sleep quality score represents good sleep quality whereas sleep quality score 3 represents poor sleep quality. Fig 1(a) records higher number of good sleep quality score which is denoted by 0, in contrast in Fig 1(b) shows significant reduction in good sleep quality score represented with 0.
Therefore, the invention intends to develop an IoT based Sleep Quality Prediction System for Cancer Patients, an innovative centralized system to monitor and predict sleep quality in cancer patients. This system contains a wearable device that acquire the data from human body through various sensors attached in the device, also an edge vision device that is used to capture the body movements of cancer patients during sleep. This data further processed, and analyse using machine learning algorithms to predict the future sleep quality of cancer patients. The wearable device is equipped with PPG sensor, accelerometer sensor, gyroscope, temperature sensors, and body position sensor in a wrist band that provide comprehensive data on sleep components, body movements, as well as body temperature. Further a rechargeable battery, storage unit, and a Wi-Fi unit is attached for internet connectivity and portable access of the smart wrist worn band. This vision device is equipped with camera, storage unit, Wi-Fi connectivity, Raspberry-Pi and neural compute stick 2 helps to get AI inference and computer vision. A cloud server is used for real time data processing and storage. Further, machine learning algorithm will be incorporated to predict future sleep quality in cancer patients by assessing the sleep components like sleep efficiency, total sleep time, sleep onset latency, wake after sleep onset, N2/N3 latency, REM latency, percentage of sleep stages, and number of awakenings. The device is integrated with a mobile/ web-based application for user access. This system can be installed in oncology hospitals to offer a convenient and effective solution for cancer patients seeking to enhance their sleep quality through advanced monitoring and predictive analytics. It is a non- invasive and less expensive system than polysomnography which is commonly used for sleep data analytics.
CN105640703A The invention relates to the technical field of intelligent wearable devices, in particular to an intelligent eye patch which attempts to improve the sleep quality of people. The intelligent eye patch comprises a sleep monitoring system used for tracking and monitoring the sleep state of a user, and gentle wakeup system used for awakening the user in a gentle manner.
RESEARCH GAP: The invention intends to predict the sleep quality in cancer patients. The invention consists a smart wrist worn band equipped PPG sensor, accelerometer, gyroscope, temperature sensors, and body position sensor that calculates different sleep stages, body movements, as well as body temperature to predict the sleep quality in cancer patients using machine learning algorithm.
US20240050029A1 A stress reduction and sleep promotion system that includes at least one remote device, at least one body sensor, and at least one remote server.
RESEARCH GAP: This invention intends to track the sleep patterns and body movements using edge vision device. A machine learning algorithm equipped mobile/ web application is used to operate the device.
CN109009067B The invention comprising a headband that user needs to wear on the head and the physiological electric signals of the human body can be automatically monitored, whether the human body is in a drowsy state is judged, the luminous component emits light to irradiate the eyelid of the user, and the secretion of melatonin of the user is restrained, so that the effects of refreshing and preventing the user are achieved.
RESEARCH GAP: The invention intends to provide a cloud based IoT system to help oncologists and cancer patients to analyse and predict their sleep quality through real-time data analytics.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to IoT based sleep quality prediction system for cancer patients.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention intends to analyse and predict the sleep quality using historical data through machine learning algorithm. Fig 2 represents the block diagram representing the components of IoT based Sleep Quality Prediction System for Cancer Patients.
IoT based sleep quality prediction system for cancer patients contains a wearable wrist band (10) equipped with PPG sensor, accelerometer, gyroscope, temperature sensors, and body position sensor acquire necessary data required to predict sleep quality in cancer patients.
This wearable wrist band in addition with edge vision device installed in a room fetch the data from human body through sensors and further sends to the cloud server (30) like (Azure, Firebase, Aliba, AWS, Watson etc.).
The cloud server is further connected to the mobile application (50) incorporating machine learning technique (40) that predicts the sleep quality on the basis of sleep components data like sleep efficiency, total sleep time, sleep onset latency, wake after sleep onset, N2/N3 latency, REM latency, percentage of sleep stages, and number of awakenings. The invention serves a hassle-free utilization of IoT devices to predict the sleep quality in cancer patients.
Fig 3 is the visual and logical representation of smart- wrist worn band that includes lily pad (11) network equipped with a series of wireless devices like memory (12) unit required for data storage, PPG sensor (13) required for heart rate, accelerometer (14) measures the change from rest position to movement of the body, gyroscope (15) that measure the rotation of the device, Wi-Fi connectivity (16) for the real time data fetching, OLED (17) display unit of the analytics, and most important battery unit (18) that provides long lasting working of the device.
Fig 4 represents the edge vision device includes Raspberry Pi for controlling the physical components, Neural compute stick (21) used for AI programming, Wi-Fi (21) for wireless connectivity, camera (23) to record the body movements and postures, 64 GB SD card (24) for data storage, input devices (25) like keyboard/ mouse, and screen as an output unit (26).
Overall, the invention intends to provide IoT based sleep quality prediction system for cancer patients with the help of wearable device equipped with sensors, an edge vision device, Wi-Fi connectivity, cloud connectivity, and a mobile application that utilizes machine learning algorithm.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention intends to analyse and predict the sleep quality using historical data through machine learning algorithm. Fig 2 represents the block diagram representing the components of IoT based Sleep Quality Prediction System for Cancer Patients.
IoT based sleep quality prediction system for cancer patients contains a wearable wrist band (10) equipped with PPG sensor, accelerometer, gyroscope, temperature sensors, and body position sensor acquire necessary data required to predict sleep quality in cancer patients.
This wearable wrist band in addition with edge vision device installed in a room fetch the data from human body through sensors and further sends to the cloud server (30) like (Azure, Firebase, Aliba, AWS, Watson etc.).
The cloud server is further connected to the mobile application (50) incorporating machine learning technique (40) that predicts the sleep quality on the basis of sleep components data like sleep efficiency, total sleep time, sleep onset latency, wake after sleep onset, N2/N3 latency, REM latency, percentage of sleep stages, and number of awakenings. The invention serves a hassle-free utilization of IoT devices to predict the sleep quality in cancer patients.
Fig 3 is the visual and logical representation of smart- wrist worn band that includes lily pad (11) network equipped with a series of wireless devices like memory (12) unit required for data storage, PPG sensor (13) required for heart rate, accelerometer (14) measures the change from rest position to movement of the body, gyroscope (15) that measure the rotation of the device, Wi-Fi connectivity (16) for the real time data fetching, OLED (17) display unit of the analytics, and most important battery unit (18) that provides long lasting working of the device.
Fig 4 represents the edge vision device includes Raspberry Pi for controlling the physical components, Neural compute stick (21) used for AI programming, Wi-Fi (21) for wireless connectivity, camera (23) to record the body movements and postures, 64 GB SD card (24) for data storage, input devices (25) like keyboard/ mouse, and screen as an output unit (26).
Overall, the invention intends to provide IoT based sleep quality prediction system for cancer patients with the help of wearable device equipped with sensors, an edge vision device, Wi-Fi connectivity, cloud connectivity, and a mobile application that utilizes machine learning algorithm.
ADVANTAGES OF THE INVENTION
a) Sleep Quality Prediction: It helps predicting sleep quality using data obtained through various components of the IoT based system.
b) Sleep Health Monitoring: Tracks vital signs and sleep stages, providing useful data to users and healthcare providers.
c) Ease of Use , Claims:1. An IoT-based sleep quality prediction system for cancer patients comprising:
A wearable wristband (10) including a PPG sensor (13), an accelerometer (14), a gyroscope (15), temperature sensors, and a body position sensor, wherein the wristband is configured to gather physiological data from the wearer;
An edge vision device (20) installed in the patient's room, comprising a Raspberry Pi, a Neural Compute Stick (21), a camera (23), and a Wi-Fi module (21), for collecting and processing data from the wristband;
A cloud server (30) connected to the edge vision device for receiving and processing data, wherein the cloud server is configured to utilize a machine learning algorithm (40) for predicting sleep quality; and
A mobile application (50) linked to the cloud server, which displays predictions based on the machine learning analysis of sleep components.
2. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the wearable wristband (10) further includes a lily pad network (11) with a memory unit (12) for data storage, an OLED display unit (17) for displaying analytics, and a battery unit (18) for powering the wristband.
3. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the edge vision device (20) includes a 64 GB SD card (24) for local data storage, input devices (25) for user interaction, and an output screen (26) for displaying processed data.
3. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the cloud server (30) is configured to utilize real-time data from the wearable wristband (10) and the edge vision device (20) to provide accurate predictions of sleep quality based on parameters including sleep efficiency, total sleep time, sleep onset latency, wake after sleep onset, N2/N3 latency, REM latency, percentage of sleep stages, and number of awakenings.
4. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the mobile application (50) provides users with interactive features to view sleep quality predictions and historical data, and allows for remote monitoring and management of the sleep quality prediction system.
5. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the machine learning algorithm (40) is configured to adapt and improve its predictions over time based on historical data collected from the wearable wristband (10) and other system components.
6. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the wearable wristband (10) is further equipped with a body temperature sensor for monitoring the wearer's body temperature throughout the night, and the collected temperature data is used to adjust the sleep quality prediction algorithm for improved accuracy.
7. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the edge vision device (20) includes an automated calibration feature that adjusts sensor sensitivity based on environmental conditions detected by the camera (23) and other sensors, ensuring consistent data quality and prediction accuracy.
8. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the cloud server (30) includes a data analytics module configured to generate personalized sleep improvement recommendations based on the machine learning analysis of historical and real-time sleep data from the wearable wristband (10) and edge vision device (20).
9. The IoT-based sleep quality prediction system as claimed in claim 1, wherein the mobile application (50) provides real-time alerts and notifications to the user regarding abnormal sleep patterns or potential issues detected by the machine learning algorithm (40), allowing for timely intervention or adjustments to improve sleep quality.