Sensor based Health Monitoring System
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This document discusses a sensor-based monitoring system designed for in-home health assessment of senior citizens, aimed at early detection of health issues through continuous and unobtrusive monitoring. The system utilizes various techniques such as principal component analysis, fuzzy pattern trees, k-nearest neighbors, neural networks, and support vector machines to analyze sensor data and generate health alerts for clinicians. The goal is to facilitate timely interventions to improve health outcomes and quality of life for older adults.
![Earlier Work
Researcher Research Conclusion
J. A. Kaye et al.
[1]
Both daytime and night time activity have been investigated
using in-home sensors.
Passive infrared (PIR) motion sensors have been used to
capture activity in a particular location in the home
M. Chan et al.
[2]
The pattern of room to room activity has been studied as a
means of investigating health changes
P. Cuddihy et
al., [3]
Motion density from PIR motion sensors (i.e., number of
events per unit time) capture overall activity level and these
have been linked with health conditions](https://image.slidesharecdn.com/seminariiippt-170716084849/85/Sensor-based-Health-Monitoring-System-6-320.jpg)
![Earlier Work(Contd..)
Researcher Research Conclusion
T. van Kasteren
et al. [4]
sleep patterns have been studied using motion
sensors bed mats or load cells
D C. Mack et al.
[5]
The detection of cognitive changes, using a
combination of motion, bed and door sensing,
medication tracking and a phone sensor for
detecting incoming and outgoing calls
M. Montero-
Odasso et al. [ 6]
Walking speed has been captured using motion
sensors ,video, radar and depth images. Walking
gait has been linked to both physical and cognitive
health](https://image.slidesharecdn.com/seminariiippt-170716084849/85/Sensor-based-Health-Monitoring-System-7-320.jpg)
Sensor based Health Monitoring System
- 1. Sensor based monitoring system for in-home Embedded Health Assessment for Senior Citizens By: Sudhanshu Janwadkar, ME-II,VLSI & Embedded Systems Guided By: Dr. M. T. Kolte, PG Coordinator, Dept of EnTC, MITCOE
- 2. Content Introduction Earlier Work Challenges in in-Home Health Assessment System Block Diagram Working: Feature Extraction Principal Component Analysis Fuzzy Pattern Tree K-Nearest Neighbour Neural Networks Support Vector Machine Working: Generating Health Alerts Conclusion
- 3. Introduction- Recently, there has been an increased focus on technology for enabling independent living and healthy aging. Identification and assessment of health issues at early stages, while they are still small, provides a window of opportunity for curing the issues before they become catastrophic Older adults will benefit from early detection and recognition of small changes in health conditions and get help early when treatment is the most effective Hence, we need an Unobtrusive, continuous monitoring in the home for the purpose of assessing early health changes
- 4. Introduction(contd..) What is in-Home Embedded Health Assessment? Sensors embedded in the environment capture behaviour and activity patterns. Changes in patterns are detected as potential signs of changing health. Based on the features extracted from in-home sensor data, health alerts are generated to clinicians Clinicians analyze each alert and provide a rating on the clinical relevance.
- 5. Introduction(Contd..) These ratings are then used as ground truth for training and testing classifiers. Thus, this system is a health change detection model Thus, this approach provides a method for detecting health problems very early, so that early treatment is possible. This method of passive in-home sensing also alleviates compliance issues.
- 6. Earlier Work Researcher Research Conclusion J. A. Kaye et al. [1] Both daytime and night time activity have been investigated using in-home sensors. Passive infrared (PIR) motion sensors have been used to capture activity in a particular location in the home M. Chan et al. [2] The pattern of room to room activity has been studied as a means of investigating health changes P. Cuddihy et al., [3] Motion density from PIR motion sensors (i.e., number of events per unit time) capture overall activity level and these have been linked with health conditions
- 7. Earlier Work(Contd..) Researcher Research Conclusion T. van Kasteren et al. [4] sleep patterns have been studied using motion sensors bed mats or load cells D C. Mack et al. [5] The detection of cognitive changes, using a combination of motion, bed and door sensing, medication tracking and a phone sensor for detecting incoming and outgoing calls M. Montero- Odasso et al. [ 6] Walking speed has been captured using motion sensors ,video, radar and depth images. Walking gait has been linked to both physical and cognitive health
- 8. Identification of best parameters to track for health change; some parameters may be too late for very early health change detection. Many seniors have multiple chronic health conditions to manage and the interaction may present changes in a variety of ways. Many seniors refuse to wear them, forget to charge batteries, or are unable to operate them Many wearable sensors are still at the prototype stage and not yet ready for long-term use in an unstructured home environment. Challenges still need to be addressed, such as unobtrusiveness, miniaturization, and robustness before they Challenges in in-Home Health Assessment
- 10. System Block Diagram (Explanation) An Embedded sensor network collects data on behaviour and activity patterns. For example: Passive infrared (PIR) motion sensors are used to capture motion in a room area and also for localized activity, e.g., in the refrigerator, in kitchen cabinets, on the ceiling over the shower etc Bed Sensors are installed beneath the bed and on the bed to capture events such as low or high pulse rate, low or high respiration rate etc Sensors for detecting every minute change in activity and behaviour pattern are installed everywhere in home Data from sensors installed in apartments are logged and stored on a secure server A one-dimensional (1-D) alert algorithm is used to generate health alerts.
- 11. System Block Diagram (Explanation) When is a health alert generated? Example: The PIR motion sensors generate an event every seven seconds if there is continuous motion. This is used as an artifact to capture the general activity level in the home by computing a motion density as motion events per unit time. For example, a resident with a sedentary lifestyle may generate only 50 motion events per hour, whereas a resident with a very active life style may generate 400 or more motion events per hour Clinicians analyze each alert using an electronic health record (EHR) based on the experience and patient history. Based on their clinical expertise, they rate the clinical
- 12. Complexity of Captured Data The fundamental observation of such in-home Embedded Health Assessment System : Huge complex web of sensors A sensor to capture change in each activity and behaviour pattern Further, each activity may be classified as High, Medium or Low OR Increasing or Decreasing The captured activity pattern is a N-dimensional feature space
- 13. Complexity of Captured Data For example, consider only following four alert parameters: bathroom activity, bed restlessness, kitchen activity, and living room activity; are considered. If both increasing and decreasing changes (the current day's count compared to the baseline period) are considered for all three time periods (daytime, night time, and full day), the dimensionality of the feature space is 24. Consider what if all sensors are taken into account
- 14. Analyzing the Feature Space In analyzing the health alerts, it is observed that some of the parameters do not typically contribute to alerts Others generate a few alert but their information is not enough to be used for supervised learning. Considering both increase and decrease in activity pattern can be redundant in some cases. Thus, observation and experience can be sufficient in some cases to reduce feature space. For others, we require complex methods like Principal Component Analysis etc
- 15. Principal Component Analysis Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than the number of original variables.
- 16. Analyzing Feature Space K Nearest Neighbour Fuzzy Pattern Tree Neural Network Support Vector Machine
- 17. K-Nearest Neighbour In pattern recognition, the k-Nearest Neighbours algorithm is a method used for classification and regression. The input consists of the k closest training examples in the feature space. The training examples are vectors in a multidimensional feature space, each with a class label. In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbours, with the object being assigned to the class most common among its k nearest neighbours k is a user-defined constant
- 18. K-Nearest Neighbour (contd..) Example:
- 19. K-Nearest Neighbour (contd..) Choose Odd Value of K for 2 value problem K must not be a multiple of number of classes The main disadvantage of K-NN method is difficulty in searching the nearest neighbours for each sample. Application Each activity pattern is considered as a input training pattern Health Alert is a output class
- 20. Fuzzy pattern tree (FPT) Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, considered to be "fuzzy". Fuzzy logic has been employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. Fuzzy logic involves linguistic variables(do not take numerical values) Degree of output may be managed by specific (membership) functions. Fuzzy Rules are framed to determine the class of output.
- 21. Fuzzy pattern tree (Contd..) Example: The meanings of the expressions cold, warm, and hot are represented by functions mapping a temperature scale. A point on that scale has three "truth values" — one for each of the three functions Since the red arrow points to zero, this temperature may be interpreted as "not hot". The orange arrow (pointing at 0.2) may describe it as "slightly warm" and the blue arrow (pointing at 0.8) "fairly cold".
- 22. Fuzzy pattern tree (Contd..) A fuzzy pattern tree is a method that uses domain knowledge and does not require training. Application The output is as follows: IF Bathroom activity for the full day is an Increase OR Bathroom activity at night time is an Increase OR Bed restlessness for the full day is an Increase OR Bed restlessness at night time is an Increase OR Kitchen activity at night time is an Increase OR Living room activity at night time is an Increase THEN Alert is Clinically Relevant Gaussian-based membership functions are used for the input parameters.
- 23. Neural Network In supervised learning, we are given a set of example pairs { (x , y); x e X, y e Y} and the aim is to find a function { f:X → Y} in the allowed class of functions that matches the examples. In other words, we wish to infer the mapping implied by the data; the cost function is related to the mismatch between our mapping and the data and it implicitly contains prior knowledge about the problem domain. A commonly used cost is the mean-squared error When one tries to minimize this cost using gradient descent for the class of neural networks using well-known back- propagation algorithm
- 24. Support Vector Machine Support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples,, an SVM training algorithm builds a model that assigns new examples into one category or the other A non-probabilistic binary linear classifier. A support vector machine constructs a hyper-plane or set of hyper-planes, which can be used for classification and regression Using Kernel trick, possible to classify inputs which are not linearly separable in that space.
- 25. Generating Health Alerts The logged sensor data are automatically analyzed on a daily basis, looking for changes in an individual's data patterns. If a change is detected for the current day, an alert email is sent to clinicians. Clinician determine whether the alert is relevant for this resident from a clinical perspective. Clinician rates the clinical relevance of the alert on a five point scale, from 1 (not clinically relevant) to 5 (very clinically relevant). This data is used for training and learning of the methods discussed and further development of the alert algorithms.
- 26. Conclusion The importance of sensor data for capturing early signs of health decline. Unobtrusive Continuous Time In-Home Embedded Health Assessment System Identifying health decline early provides a window of opportunity for early treatment and intervention that can address health problems before they become catastrophic. This offers the potential for improved health outcomes, reduced healthcare costs, continued independence and better quality of life.
- 27. Thank-you
- 28. References Marjorie Skubic, Rainer Dane Guevara, Marilyn Rantz, "Automated Health Alerts Using In- Home Sensor Data for Embedded Health Assessment", IEEE Journal of Translational Engineering and Medicine, Volume 3, 2015,pp2168-2372 1. J. A. Kaye et al., ``Intelligent systems for assessing aging changes: Homebased, unobtrusive, and continuous assessment of aging,'' J. Gerontol., Psychol. Sci., vol. 66B, no. 1, pp. i180i190, 2011. 2. M. Chan, E. Campo, and D. Esteve, ``Assessment of activity of elderly people using a home monitoring system,'' Int. J. Rehabil. Res., vol. 28, no. 1, pp. 6976, 2005. 3. P. Cuddihy et al., ``Successful aging,'' IEEE Pervasive Comput., vol. 3, no. 2, pp. 4850, Apr. 2004. 4. T. van Kasteren, A. K. Noulas, G. Englebienne, and B. Kröse, ``Accurate activity recognition in a home setting,'' in Proc. 10th Int. Conf. Ubiquitous Comput., 2008, pp. 19. 5. D. C. Mack, J. T. Patrie, P. M. Suratt, R. A. Felder, and M. Alwan, ``Development and preliminary validation of heart rate and breathing rate detection using a passive, ballistocardiography-based sleep monitoring system,'' IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 1, pp. 111120, Jan. 2009. 6. J. Kaye et al., ``Unobtrusive measurement of daily computer use to detect mild cognitive impairment,'' Alzheimer's Dementia, vol. 10, no. 1, pp. 1017, 2014. 7. M. J. Rantz, ``Evaluation of health alerts from an early illness warnin system in independent living,'' Comput., Inform., Nursing, vol. 31, no. 6, pp. 274280, 2013.
Editor's Notes
- #11: Passive infrared (PIR) motion sensors are used to capture motion in a room area and also for localized activity, e.g., in the refrigerator, in kitchen cabinets, on the ceiling over the shower, and on the ceiling over the front door to detect apartment exits. The PIR motion sensors, which use the wireless X-10 protocol for data transmission, generate an event every seven seconds if there is continuous motion. This is used as an artifact to capture the general activity level in the home by computing a motion density as motion events per unit time. For example, a resident with a sedentary lifestyle may generate only 50 motion events per hour, whereas a resident with a very active life style may generate 400 or more motion events per hour A pneumatic bed sensor is installed on the bed mattress and used to capture sleep patterns. The bed sensor generates events for restlessness in bed (four levels) as well as low, normal, and high events for pulse rate and respiration rate. For those residents who often sleep in a recliner chair, the bed sensor is installed in the chair. Sensor networks with motion, bed, chair and stove sensing have been deployed. Automated monitoring is used to detect the absence of sensor data, e.g., in the case of battery failures. However, there is still some data loss due to the brittleness of the X10 transmission.