Search Results (36)

There is currently no effective therapeutic capable of arresting or inducing regression of primary or metastatic brain cancers. This article presents both pre-clinical and clinical studies supportive that a new bioengineered technology could induce regression and/or elimination of primary and metastatic brain cancers through three disease-modifying mechanisms. Transcranial Radiofrequency Wave Treatment (TRFT) is non-thermal, non-invasive and self-administered in-home to safely provide radiofrequency waves to the entire human brain. Since TRFT has already been shown to stop and reverse the cognitive decline of Alzheimer’s Disease in small studies, evidence is provided that three key mechanisms of TRFT action, alone or in synergy, could effectively treat brain cancers: (1) enhancement of brain meningeal lymph flow to increase immune trafficking between the brain cancer and cervical lymph nodes, resulting in a robust immune attack on the brain cancer; (2) rebalancing of the immune system’s cytokines within the brain or brain cancer environment to decrease inflammation therein and thus make for an inhospitable environment for brain cancer growth; (3) direct anti-proliferation/antigrowth affects within the brain tumor microenvironment. Importantly, these mechanisms of TRFT action could be effective against both visualized brain tumors and those that are yet too small to be identified through brain imaging. The existing animal and human clinical evidence presented in this perspective article justifies TRFT to be clinically tested immediately against both primary and metastatic brain cancers as monotherapy or possibly in combination with immune checkpoint inhibitors. Full article

In-home activities are inevitably important parts of individuals’ daily schedules, as people spend more time working and doing various other activities (e.g., online shopping or banking) at home. However, conventional activity-based travel demand models (ABMs) only consider travel and travel-related out-of-home activities, ignoring the interaction between in-home and out-of-home activities. To fill in this gap and increase the understanding of what people do at home and how in-home and out-of-home activities affect each other, a new method is proposed in this study. The approach predicts the types and durations of in-home activities of daily schedules generated by ABMs. In model building, statistical methods such as multinomial logit, log-linear regression, and activity sequential information are utilized, while in calibration, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is employed. The proposed method was tested using training data and by applying the approach to the schedules of 6.3 million people in the Flemish region of Belgium generated by a representative ABM. Based on the statistical methods, the mean absolute errors were 0.36 and 0.21 for predicting the number and sum of the durations of in-home activities (over all types) per schedule, respectively. The prediction obtained a 10% and 8% improvement using sequential information. After calibration, an additional 60% and 68% were gained regarding activity participation rates and time spent per day. The experimental results demonstrate the potential and practical ability of the proposed method for the incorporation of in-home activities in activity–travel schedules, contributing towards the extension of ABMs to a wide range of applications that are associated with individuals’ in-home activities (e.g., the appropriate evaluation of energy consumption and carbon emission estimation as well as sustainable policy designs for telecommuting). Full article

To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3–5 m. Full article

Introduction: This study aimed to validate the ability of a prototype sport watch (Polar Electro Oy, FI) to recognize wake and sleep states in two trials with and without an interval training session (IT) 6 h prior to bedtime. Methods: Thirty-six participants completed this study. Participants performed a maximal aerobic test and three polysomnography (PSG) assessments. The first night served as a device familiarization night and to screen for sleep apnea. The second and third in-home PSG assessments were counterbalanced with/without IT. Accuracy and agreement in detecting sleep stages were calculated between PSG and the prototype. Results: Accuracy for the different sleep stages (REM, N1 and N2, N3, and awake) as a true positive for the nights without exercise was 84 ± 5%, 64 ± 6%, 81 ± 6%, and 91 ± 6%, respectively, and for the nights with exercise was 83 ± 7%, 63 ± 8%, 80 ± 7%, and 92 ± 6%, respectively. The agreement for the sleep night without exercise was 60.1 ± 8.1%, k = 0.39 ± 0.1, and with exercise was 59.2 ± 9.8%, k = 0.36 ± 0.1. No significant differences were observed between nights or between the sexes. Conclusion: The prototype showed better or similar accuracy and agreement to wrist-worn consumer products on the market for the detection of sleep stages with healthy adults. However, further investigations will need to be conducted with other populations. Full article

Eating outside-of-home (EOH) is one of the main changes in lifestyle that occurred worldwide in the past few decades. Given that EOH behavior is influenced by individual and contextual factors, the utilization of a theory seems to be suitable in analyzing this health behavior. The fourth-generation theory multi-theory model (MTM) is designed exclusively for health behavior change at the individual and community levels. Therefore, the purpose of this analytical cross-sectional study was to investigate EOH behavior by using the MTM among a nationally representative sample in the United States (US). Data for this study were collected from April–May 2023 via a 61-item psychometric valid, web-based, structured survey disseminated via Qualtrics. Chi-square/Fisher’s exact tests were used to compare categorical data, whereas the independent-samples t-test was used to compare the mean scores of MTM constructs across groups. Pearson correlation analysis was performed for the intercorrelation matrix between the MTM constructs and hierarchical regression models were built to predict the variance in the initiation and sustenance by certain predictor variables beyond demographic characteristics. The p values in the multiple comparisons were calculated by using adjusted residuals. Among a total of 532 survey respondents, 397 (74.6%) indicated being engaged in EOH at least twice a week, whereas 135 (25.4%) reported not being engaged in EOH. People who were engaged in EOH were younger (mean age = 42.25 ± 17.78 years vs. 55.89 ± 19.43 years) African American, (15.9% vs. 6.7%, p = 0.01), single or never married, (34.0% vs. 23.0%, p = 0.02), had a graduate degree (9.6% vs. 3.7%, p = 0.03), and were employed (72.0% vs. 34.8%, p < 0.001) as opposed to those who reported not being engaged in eating outside the home. Among the MTM constructs of initiation, “behavioral confidence” and “changes in the physical environment” were the significant predictors of initiating a reduction in EOH behavior and explained 48% of the variance in initiation. Among the MTM constructs of sustenance, “emotional transformation” and “changes in the social environment” were the significant predictors of sustaining a reduction in EOH behavior and explained 50% of the variance in sustenance. This study highlights a need to design MTM-based educational interventions that promote in-home eating instead of frequent EOH for health, family bonding, economic, and other reasons. Full article

The COVID-19 pandemic caused by the virus SARS-CoV-2 was the greatest global threat to human health in the last three years. The most widely used methodologies for the diagnosis of COVID-19 are quantitative reverse transcription polymerase chain reaction (RT-qPCR) and rapid antigen tests (RATs). PCR is time-consuming and requires specialized instrumentation operated by skilled personnel. In contrast, RATs can be used in-home or at point-of-care but are less sensitive, leading to a higher rate of false negative results. In this work, we describe the development of a disposable, electrochemical, and laser-scribed graphene-based biosensor strips for COVID-19 detection that exploits a split-ester bond ligase system (termed ‘EsterLigase’) for immobilization of a virus-specific nanobody to maintain the out-of-plane orientation of the probe to ensure the efficacy of the probe-target recognition process. An anti-spike VHH E nanobody, genetically fused with the EsterLigase domain, was used as the specific probe for the spike receptor-binding domain (SP-RBD) protein as the target. The recognition between the two was measured by the change in the charge transfer resistance determined by fitting the electrochemical impedance spectroscopy (EIS) spectra. The developed LSG-based biosensor achieved a linear detection range for the SP-RBD from 150 pM to 15 nM with a sensitivity of 0.0866 [log(M)]⁻¹ and a limit of detection (LOD) of 7.68 pM. Full article

The vast majority of fire-related deaths occur in residential buildings. Until recently, the fire risk for these buildings was only considered through static risk assessments or period-based assessments applying to certain periods of the year, e.g., Christmas holidays. However, for homes with indoor wooden panelling, especially in the ceiling, a dynamic fire danger indicator can be predicted for cold climate regions. Recognising the effect of fuel moisture content (FMC) of indoor wooden panelling on the enclosure fire development allows for the prediction of a wooden home fire danger indicator. In the present study, dry wood fire dynamics are analysed and experimental observations are reported to support in-home wooden panel FMC as a suitable wooden home fire danger indicator. Then, from previous work, the main equation for modelling in-home FMC is considered and a generic enclosure for FMC modelling is justified based on literature data and supported through a sensitivity study for Norwegian wooden homes. Further, ten years of weather data for three selected locations in Norway, i.e., a coastal town, an inland fjord town and a mountain town, were analysed using a three-dimensional risk matrix to assess the usability of the fire risk modelling results. Finally, a cold climate wooden home fire danger index was introduced to demonstrate how the risk concept can be communicated in an intuitive way using similar gradings as the existing national forest fire index. Based on the generic enclosure, the findings support FMC as a fire risk indicator for homes with interior wooden panelling (walls and ceiling). Large differences in the number of days with arid in-home conditions were identified for the selected towns. The number of days with combined strong wind and dry wooden homes appears to depend more on the number of days with strong wind than days of in-home drought. Thus, the coastal town was more susceptible to conflagrations than the drier inland towns. This aligns well with the most significant fire disasters in Norway since 1900. In addition, it was demonstrated that the number of high-risk periods is manageable and can be addressed by local fire departments through proactive measures. In turn, the fire risk modelling and associated index respond well to the recent changes in Norwegian regulations, requiring the fire departments to have systems for detecting increased risk levels. Testing the modelling for a severe winter fire in the USA indicates that the presented approach may be of value elsewhere as well. Full article

Those who survive the initial incidence of a stroke experience impacts on daily function. As a part of the rehabilitation process, it is essential for clinicians to monitor patients’ health status and recovery progress accurately and consistently; however, little is known about how patients function in their own homes. Therefore, the goal of this study was to develop, train, and test an algorithm within an ambient, in-home depth sensor system that can classify and quantify home activities of individuals post-stroke. We developed the Daily Activity Recognition and Assessment System (DARAS). A daily action logger was implemented with a Foresite Healthcare depth sensor. Daily activity data were collected from seventeen post-stroke participants’ homes over three months. Given the extensive amount of data, only a portion of the participants’ data was used for this specific analysis. An ensemble network for activity recognition and temporal localization was developed to detect and segment the clinically relevant actions from the recorded data. The ensemble network, which learns rich spatial-temporal features from both depth and skeletal joint data, fuses the prediction outputs from a customized 3D convolutional–de-convolutional network, customized region convolutional 3D network, and a proposed region hierarchical co-occurrence network. The per-frame precision and per-action precision were 0.819 and 0.838, respectively, on the test set. The outcomes from the DARAS can help clinicians to provide more personalized rehabilitation plans that benefit patients. Full article

Decreased arterial perfusion is a typical condition of patients with peripheral artery disease (PAD), with the microvascular picture particularly present among women. This observational study aimed to detect foot perfusion changes by infrared thermography (IRT) after a home-based exercise program in both sexes. A total of 76 PAD patients with claudication (72 ± 4 years; 52 males) were enrolled in a structured in-home exercise program composed of two daily 8 min interval walking sessions (1:1 walk:rest ratio) with progressively increasing speed. Outcome measures collected at baseline (T0) and at each hospital visit after 5 weeks, 12 weeks and 20 weeks included foot temperature measured by IRT (anterior tibial, posterior tibial, dorsalis pedis and arcuate artery regions), ankle brachial index and the 6 min walking test. After 20 weeks, foot temperature in both limbs showed a significant increasing trend, with a mean variation of 1.3 °C for the more impaired limb and 0.9 °C for the contralateral limb (t = 8.88, p < 0.001 and t = 5.36; p < 0.001, respectively), with significant changes occurring after 5 weeks of training. The sex-oriented analysis did not highlight any significant difference, with an improvement of mean foot temperature of 1.5 ± 0.6 °C in females versus 1.2 ± 0.5 °C in males (p = 0.42). Ankle brachial index and performance also significantly improved over time (p < 0.001) without gender differences. In patients with PAD, a structured low-intensity exercise program significantly improved foot temperature and exercise capacity without any sex-related difference. Full article

Background: Although exposure to chemical pesticides is known to cause negative effects on human health, farmers in Ban Luang, Nan, Thailand, continue to use them regularly to protect crops. This study focused on mothers who were engaged in farm tasks and had children between the ages of 0 to 72 months, with the objective of reducing pesticide exposure. Methods: This study was conducted from May 2020 to October 2020 in the Ban Fa and Ban Phi sub-districts in Ban Luang due to the high use of pesticides in these areas. A systematic random sampling technique was used to recruit 78 mothers exposed to pesticides. Thirty-nine mothers from Ban Fa district were randomly assigned to the intervention group and 39 from Ban Phi to the control group over a 3-month period. This study applied a pesticide behavioral change training program for the intervention group. To assess the effectiveness of the program, the study compared the results of a questionnaire regarding knowledge, attitude, and practice (KAP) and health beliefs related to pesticide exposure as well as the levels of acetylcholinesterase (AChE) and butyryl cholinesterase (BChE) enzymes, biomarkers of exposure to pesticides, before and after the intervention using ANCOVA statistical test. Furthermore, to evaluate the effectiveness of the intervention program, a paired t-test was used to investigate the in-home pesticide safety assessment. Results: After the intervention, we observed no significant change in AChE; however, a significant improvement in BChE (p < 0.05), a marker of short-term recovery, was observed. Pesticides can cause a reduction in AChE and BChE, however, after eliminating pesticides, BChE takes a shorter time (about 30–50 days) to recover than AChE (around 90–120 days). Therefore, increases in the measured concentrations of AChE and/or BChE suggest the presence of less chemicals from pesticides in the human body. The study also found a significant improvement in KAP and beliefs about chemical pesticide exposure after the intervention (p < 0.05). Furthermore, using a paired t-test, we found a significant increase in pesticide safety practices (p < 0.05) in the intervention group and a borderline significant increase regarding in-home safety (p = 0.051) in the control group. Conclusions: Based on the results, the constructs of the intervention program were effective and could be applied in other agricultural areas in less developed countries. However, due to time limitations during the COVID-19 pandemic, further studies should be conducted to enable data collection over a longer time, with a larger number of subjects providing the ChE levels for the non-agricultural season. Full article

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients. Full article

In the nine months leading up to COVID-19, our biomedical engineering research group was in the very early stages of development and in-home testing of HUGS, the Hand Use and Grasp Sensor (HUGS) system. HUGS was conceived as a tool to allay parents’ anxiety by empowering them to monitor their infants’ neuromotor development at home. System focus was on the evolving patterns of hand grasp and general upper extremity movement, over time, in the naturalistic environment of the home, through analysis of data captured from force-sensor-embedded toys and 3D video as the baby played. By the end of March, 2020, as the COVID-19 pandemic accelerated and global lockdown ensued, home visits were no longer possible and HUGS system testing ground to an abrupt halt. In the spring of 2021, still under lockdown, we were able to resume recruitment and in-home testing with HUGS-2, a system whose key requirement was that it be contactless. Participating families managed the set up and use of HUGS-2, supported by a detailed library of video materials and virtual interaction with the HUGS team for training and troubleshooting over Zoom. Like the positive/negative poles of experience reported by new parents under the isolation mandated to combat the pandemic, HUGS research was both impeded and accelerated by having to rely solely on distance interactions to support parents, troubleshoot equipment, and securely transmit data. The objective of this current report is to chronicle the evolution of HUGS. We describe a system whose design and development straddle the pre- and post-pandemic worlds of family-centered health technology design. We identify and classify the clinical approaches to infant screening that predominated in the pre-COVID-19 milieu and describe how these procedural frameworks relate to the family-centered conceptualization of HUGS. We describe how working exclusively through the proxy of parents revealed the family’s priorities and goals for child interaction and surfaced HUGS design shortcomings that were not evident in researcher-managed, in-home testing prior to the pandemic. Full article

Parkinson’s disease (PD) is a neurological progressive movement disorder, affecting more than 10 million people globally. PD demands a longitudinal assessment of symptoms to monitor the disease progression and manage the treatments. Existing assessment methods require patients with PD (PwPD) to visit a clinic every 3–6 months to perform movement assessments conducted by trained clinicians. However, periodic visits pose barriers as PwPDs have limited mobility, and healthcare cost increases. Hence, there is a strong demand for using telemedicine technologies for assessing PwPDs in remote settings. In this work, we present an in-home telemedicine kit, named iTex (intelligent Textile), which is a patient-centered design to carry out accessible tele-assessments of movement symptoms in people with PD. iTex is composed of a pair of smart textile gloves connected to a customized embedded tablet. iTex gloves are integrated with flex sensors on the fingers and inertial measurement unit (IMU) and have an onboard microcontroller unit with IoT (Internet of Things) capabilities including data storage and wireless communication. The gloves acquire the sensor data wirelessly to monitor various hand movements such as finger tapping, hand opening and closing, and other movement tasks. The gloves are connected to a customized tablet computer acting as an IoT device, configured to host a wireless access point, and host an MQTT broker and a time-series database server. The tablet also employs a patient-centered interface to guide PwPDs through the movement exam protocol. The system was deployed in four PwPDs who used iTex at home independently for a week. They performed the test independently before and after medication intake. Later, we performed data analysis of the in-home study and created a feature set. The study findings reported that the iTex gloves were capable to collect movement-related data and distinguish between pre-medication and post-medication cases in a majority of the participants. The IoT infrastructure demonstrated robust performance in home settings and offered minimum barriers for the assessment exams and the data communication with a remote server. In the post-study survey, all four participants expressed that the system was easy to use and poses a minimum barrier to performing the test independently. The present findings indicate that the iTex glove system has the potential for periodic and objective assessment of PD motor symptoms in remote settings. Full article

Sensory professionals are looking for alternative ways to conduct laboratory sensory testing, especially central location testing (CLT), during the COVID-19 pandemic. One way could be conducting CLTs at home (i.e., in-home testing). It is questionable whether food samples under in-home testing should be presented in uniform utensils, as it does so under laboratory sensory testing. This study aimed to determine whether utensil conditions could affect consumer perception and acceptance of food samples evaluated under in-home testing. Sixty-eight participants (40 females and 28 males) prepared chicken-flavored ramen noodle samples and evaluated them for attribute perception and acceptance, under two utensil conditions, using either their utensils (“Personal”) or uniform utensils provided (“Uniform”). Participants also rated their liking of forks/spoons, bowls, and eating environments, respectively, and attentiveness to sensory evaluation under each utensil condition. Results of the in-home testing showed that participants liked ramen noodle samples and their flavors under the “Personal” condition significantly more than under the “Uniform” condition. Ramen noodle samples evaluated under the “Uniform” condition were significantly higher in terms of saltiness than those evaluated under the “Personal” condition. Participants liked forks/spoons, bowls, and eating environments used under the “Personal” condition significantly more than those used under the “Uniform” condition. While overall likings of ramen noodle samples, evaluated under the “Personal” condition, significantly increased with an increase in hedonic ratings of forks/spoons or bowls, such significant correlations were not observed under the “Uniform” condition. In other words, providing uniform utensils (forks, spoons, and bowls) to participants in the in-home testing can reduce the influences of utensils on consumer likings of ramen noodle samples evaluated at home. In conclusion, this study suggests that sensory professionals should consider providing uniform utensils when they want to focus solely on consumer perception and acceptance of food samples by minimizing influences of environmental contexts, especially utensils, in the “in-home” testing. Full article

Increasing concern over air pollution has led to the development of low-cost sensors suitable for wide-scale deployment and use by citizen scientists. This project investigated the AirU low-cost particle sensor using two methods: (1) a comparison of pre- and post-deployment calibration equations for 24 devices following use in a field study, and (2) an in-home comparison between 3 AirUs and a reference instrument, the GRIMM 1.109. While differences (and therefore some sensor degradation) were found in the pre- and post-calibration equation comparison, absolute value changes were small and unlikely to affect the quality of results. Comparison tests found that while the AirU did tend to underestimate minimum and overestimate maximum concentrations of particulate matter, ~88% of results fell within ±1 μg/m³ of the GRIMM. While these tests confirm that low-cost sensors such as the AirU do experience some sensor degradation over multiple months of use, they remain a valuable tool for exposure assessment studies. Further work is needed to examine AirU performance in different environments for a comprehensive survey of capability, as well as to determine the source of sensor degradation. Full article