Forecasting behavior in smart homes based on sleep and wake patterns

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids

BACKGROUND

Novel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks.

AIM

This study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone.

METHODS

Participants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data.

RESULTS

Smart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%).

CONCLUSIONS

Smart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids.

Evaluating machine learning models to classify occupants' perceptions of their indoor environment and sleep quality from indoor air quality

A variety of factors can affect a person's perception of their environment and health, but one factor that is often overlooked in indoor settings is the air quality. To address this gap, we develop and evaluate four Machine Learning (ML) models on two disparate datasets using Indoor Air Quality (IAQ) parameters as primary features and components of self-reported IAQ satisfaction and sleep quality as target variables. In each case, we compare models to each other as well as to a simple model that always predicts the majority outcome. In the first analysis, we use open-source data collected from 93 California residences to predict occupant's satisfaction with their indoor environment. Results indicate building ventilation rate, Relative Humidity (RH), and formaldehyde are most influential when predicting IAQ perception and do so with an accuracy greater than the simplified model. The second analysis uses IAQ data gathered from a field study we conducted with 20 participants over 11 weeks to train similar models. We obtain accuracy and F1 scores similar to the simplified model where PM_2.5 and TVOCs represent the most important predictors. Our results underscore the ability of IAQ to affect a person's perception of their built environment and health and highlight the utility of ML models to explore the strength of these relationships.Implications: The results from this study show that two outcome variables - occupant's indoor air quality (IAQ) satisfaction and perceived sleep quality - are related to the measured IAQ parameters but not heavily influenced by typical values measured in apartments and homes. This study highlights the ability of machine learning models as exploratory analysis tools to determine underlying relationships within and across datasets in addition to understanding the importance of certain features on the outcome variable. We compare four different models and find that the random forest classifier has the best performance in both analysis on IAQ satisfaction and perceived sleep quality. It is a suitable model for predicting IAQ-related subjective metrics and also provides value insight into the feature importance of the IAQ parameters. The accuracy of any of these machine learning models in predicting occupants' comfort or sleep quality is limited by the dataset size, how data is collected, and range of data. This study identifies the factors that are important to IAQ perception: ventilation rate, relative humidity, and concentrations of formaldehyde, NO2, and particulate matter. It indicates that sensors that can measure these variables are necessary for future, related studies that model occupants' IAQ satisfaction. However, this study does not find strong relationships between any of the IAQ parameters measured in this study and perceived sleep quality despite the logical pathway between these many pollutants and respiratory issues. A prediction model of IAQ perception or sleep quality can be integrated into home management systems to automatically adjust building operations such as ventilation rates in smart buildings. Once buildings are equipped with a network of low-cost sensors that measure concentrations of pollutants and operating conditions of the ventilation system, the prediction model can be used to predict the occupants' comfort and facilitate the control of the ventilation system.

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

BACKGROUND

Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.

OBJECTIVE

The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.

METHODS

We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.

RESULTS

We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

Toward Improved Treatment and Empowerment of Individuals With Parkinson Disease: Design and Evaluation of an Internet of Things System

Background

Parkinson disease (PD) is a chronic degenerative disorder that causes progressive neurological deterioration with profound effects on the affected individual’s quality of life. Therefore, there is an urgent need to improve patient empowerment and clinical decision support in PD care. Home-based disease monitoring is an emerging information technology with the potential to transform the care of patients with chronic illnesses. Its acceptance and role in PD care need to be elucidated both among patients and caregivers.

Objective

Our main objective was to develop a novel home-based monitoring system (named EMPARK) with patient and clinician interface to improve patient empowerment and clinical care in PD.

Methods

We used elements of design science research and user-centered design for requirement elicitation and subsequent information and communications technology (ICT) development. Functionalities of the interfaces were the subject of user-centric multistep evaluation complemented by semantic analysis of the recorded end-user reactions. The ICT structure of EMPARK was evaluated using the ICT for patient empowerment model.

Results

Software and hardware system architecture for the collection and calculation of relevant parameters of disease management via home monitoring were established. Here, we describe the patient interface and the functional characteristics and evaluation of a novel clinician interface. In accordance with our previous findings with regard to the patient interface, our current results indicate an overall high utility and user acceptance of the clinician interface. Special characteristics of EMPARK in key areas of interest emerged from end-user evaluations, with clear potential for future system development and deployment in daily clinical practice. Evaluation through the principles of ICT for patient empowerment model, along with prior findings from patient interface evaluation, suggests that EMPARK has the potential to empower patients with PD.

Conclusions

The EMPARK system is a novel home monitoring system for providing patients with PD and the care team with feedback on longitudinal disease activities. User-centric development and evaluation of the system indicated high user acceptance and usability. The EMPARK infrastructure would empower patients and could be used for future applications in daily care and research.

Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study

Background

Sleep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring.

Objective

The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations.

Methods

From the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ≥6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household’s record was divided into different subsets based on seasonal, weekday, and seasonal weekday scales.

Results

Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450; P<.001; odds ratio [OR] 1.8, 95% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450; P<.001; OR 1.8, 95% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450; P<.001; OR 5.6, 95% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445; P<.001; OR 2.06, 95% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455; P<.001; OR 1.6, 95% CI 1.3-2.3). These results are in accordance with existing literature.

Conclusions

This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change).

Hands-Free Authentication for Virtual Assistants with Trusted IoT Device and Machine Learning

Virtual assistants, deployed on smartphone and smart speaker devices, enable hands-free financial transactions by voice commands. Even though these voice transactions are frictionless for end users, they are susceptible to typical attacks to authentication protocols (e.g., replay). Using traditional knowledge-based or possession-based authentication with additional invasive interactions raises users concerns regarding security and usefulness. State-of-the-art schemes for trusted devices with physical unclonable functions (PUF) have complex enrollment processes. We propose a scheme based on a challenge response protocol with a trusted Internet of Things (IoT) autonomous device for hands-free scenarios (i.e., with no additional user interaction), integrated with smart home behavior for continuous authentication. The protocol was validated with automatic formal security analysis. A proof of concept with websockets presented an average response time of 383 ms for mutual authentication using a 6-message protocol with a simple enrollment process. We performed hands-free activity recognition of a specific user, based on smart home testbed data from a 2-month period, obtaining an accuracy of 97% and a recall of 81%. Given the data minimization privacy principle, we could reduce the total number of smart home events time series from 7 to 5. When compared with existing invasive solutions, our non-invasive mechanism contributes to the efforts to enhance the usability of financial institutions' virtual assistants, while maintaining security and privacy.

Probabilistic elderly person's mood analysis based on its activities of daily living using smart facilities

The world's population is aging, and eldercare services that use smart facilities such as smart homes are widely common in societies now. With the aid of smart facilities, the present study aimed at understanding an elder's moods based on the person’s activities of daily living (ADLs). With this end in view, an explainable probabilistic graphical modeling approach, applying the Bayesian network (BN), was proposed. The proposed BN-based model was capable of defining the relationship between the elder's ADLs and moods in three different levels: Activity-based Feature (AbF), Category of Activity (CoA), and the mood state. The model also allowed us to explain the transformations among the different levels/nodes on the defined BNs. A framework featured with smart facilities, including a smart home, a smartphone, and a wristband, was utilized to assess the model. The smart home was an elderly woman's house, equipped with a set of binary-based sensors. For about five months, the ADLs' data have been recorded through daily behavioral-based information, registered by experts using a defined questionnaire. The obtained results proved that the proposed BN-based model of the current study could promisingly estimate the elder's moods and CoA states. Moreover, in contrast to the machine learning techniques that behave like a black box, the effect of each feature from the lower levels to the higher levels of information of the BNs can be traced. Implications of the findings for future diagnosis and treatment of the elderly are considered.

An entropy-based approach to the study of human mobility and behavior in private homes

Understanding human mobility in outdoor environments is critical for many applications including traffic modeling, urban planning, and epidemic modeling. Using data collected from mobile devices, researchers have studied human mobility in outdoor environments and found that human mobility is highly regular and predictable. In this study, we focus on human mobility in private homes. Understanding this type of human mobility is essential as smart-homes and their assistive applications become ubiquitous. We model the movement of a resident using ambient motion sensor data and construct a chronological symbol sequence that represents the resident's movement trajectory. Entropy rate is used to quantify the regularity of the resident's mobility patterns, and an upper bound of predictability is estimated. However, the presence of visitors and malfunctioning sensors result in data that is not representative of the resident's mobility patterns. We apply a change-point detection algorithm based on penalized contrast function to detect these changes, and to identify the time periods when the data do not completely reflect the resident's activities. Experimental results using the data collected from 10 private homes over periods of 178 to 713 days show that human mobility at home is also highly predictable in the range of 70% independent of variations in floor plans and individual daily routines.

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis

BACKGROUND

Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain.

OBJECTIVE

This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain.

METHODS

A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem.

RESULTS

We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42.

CONCLUSIONS

Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

Smart Secure Homes: A Survey of Smart Home Technologies that Sense, Assess, and Respond to Security Threats

Smart home design has undergone a metamorphosis in recent years. The field has evolved from designing theoretical smart home frameworks and performing scripted tasks in laboratories. Instead, we now find robust smart home technologies that are commonly used by large segments of the population in a variety of settings. Recent smart home applications are focused on activity recognition, health monitoring, and automation. In this paper, we take a look at another important role for smart homes: security. We first explore the numerous ways smart homes can and do provide protection for their residents. Next, we provide a comparative analysis of the alternative tools and research that has been developed for this purpose. We investigate not only existing commercial products that have been introduced but also discuss the numerous research that has been focused on detecting and identifying potential threats. Finally, we close with open challenges and ideas for future research that will keep individuals secure and healthy while in their own homes.

Activity Learning as a Foundation for Security Monitoring in Smart Homes

Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed.