[Practical Implementation of Gerontechnology Smart Products: Product Suitability Assessment].

Because of the critical importance to national healthcare of developing and applying smart technologies to address the needs of Taiwan's rapidly aging society, "gerontechnology" is an industry sector that must be promoted to achieve related policy goals. The scope of technologies involved in gerontechnology is broad, with the application of smart technology often the focus of industrial development. The most crucial feature of gerontechnology-related "smart" products is the collection, analysis, and feedback of target data in the application process necessary to facilitate the provision of "precision" aged care. The application of smart technology to meet the rapidly increasing life and care needs of older adults in aging societies is increasingly valued worldwide. However, the practical implementation of smart technology in aged care is still not widespread, and thus represents an urgent problem to be solved. There are many aspects to consider in the practical application of gerontechnology, among which caregivers are the key to practical application, with product suitability being the first concern of caregivers. In this article, a process is proposed to allow caregivers to assess the suitability of gerontechnology smart products in terms of "data sensing and transmission," "indicator identification and analysis," "event prediction and feedback," and "intervention measures." Based on the World Health Organization's concept of implementing research in healthcare, decision-making evaluation indicators for product selection and a product suitability questionnaire for institutional care are proposed with the goal of providing a practical tool for care institutions to assess the suitability of gerontechnology smart products.

Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care.

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data.

Implementation and experience of an innovative smart patient care system: a cross-sectional study.

BACKGROUND: Although a patient care system may help nurses handle patients' requests or provide timely assistance to those in need, there are a number of barriers faced by nurses in handling alarms. METHODS: The aim of the study was to describe the implementation and experience of an innovative smart patient care system (SPCS). This study applied a cross-sectional descriptive design. We recruited 82 nurses from a medical center in Taiwan, with 25 nurses from a ward that had introduced an SPCS and 57 nurses from wards that used the traditional patient care system (TPCS). The major advantages of the SPCS compared to the TPCS include the specification of alarm purposes, the routing of alarms directly to the mobile phone; the capability of immediate communication via phone; and three-stage bed-exit alerts with low false alarm rate. RESULTS: Approximately 56% of nurses in the TPCS wards perceived that the bed-exit alert was easily ignorable, while this rate was reduced to 32% in the SPCS ward. The immediate communication via phone was considered as the most helpful function of the SPCS, with a weighted average score of 3.92/5, and 52% of nurses strongly agreed (5/5) that this function was helpful. The second-highest ranked function was the three-stage bed-exit alert, with an average score of 3.68/5, with approximately 24% of nurses strongly agreeing (5/5) that this function was helpful. The average response time using TPCS was 145.66 s while it was 59.02 s using the SPCS (P < .001). Among the 110 observed alarms in the SPCS ward, none of them were false bed-exit alarms. In comparison, among 120 observed alarms in the TPCS wards, 42 (35%) of them were false bed-exit alarms (P < .001). In this study, we found that 30.91% of alarms using SPCS were processed because nurses received and responded to the alert via mobile phone. CONCLUSIONS: A smart patient care system is needed to help nurses make more informed prioritization decisions between responding to alarms and ongoing tasks and finally assist them in adjusting their work in various situations to improve work efficiency and care quality.

Dynamic white lighting to aid sleep and vision for persons living with dementia using off-the-shelf LED strips.

Alzheimer disease and related dementias affect 15-20% of elderly people, and 60-70% of these suffer from sleep disturbances. Studies suggest that lighting can improve sleep. The key challenge is how to deliver light effectively. We have designed a lighting system that adjusts spectrum and irradiance on a 24-hour timetable to provide spatially uniform, shadow-free white light with CRI>85 and up to 1000 Lux for day vision and amber light for night vision. To aid sleep, melanopic illuminance varies over 3 orders of magnitude to enable strong suppression of melatonin in the morning/early afternoon, moderate suppression in the evening, and no suppression at night.

Relationship between a pressure redistributing foam mattress and pressure injuries: An observational prospective cohort study.

BACKGROUND AND PURPOSE: Pressure injuries remain a significant health care issue in various settings. The purpose of this study was to examine the relationship between a pressure redistributing foam mattress (PRFM) and the development of pressure injuries. METHODS: This study employed an observational prospective cohort study design. We enrolled 254 participants from the intensive care unit who were at risk of developing pressure injuries. Participants were exposed to either a nonpressure redistributing foam mattress (NPRFM), which was the standard mattress used at the study site, or a PRFM made of viscoelastic, temperature-sensitive, polyurethane memory foam. The patients' assignment to either a PRFM or NPRFM was performed upon their admission, before the study eligibility screening. The relationship between the PRFM and the development of pressure injuries was studied using a logistic regression model. RESULTS: The overall incidence of pressure injuries was 5.9% (15/254) in our study, with 1.6% (2/127) for participants who used a PRFM and 10.2% (13/127) for those using a NPRFM. After adjusting for potential confounding variables, use of a PRFM was associated with an 88% reduced risk of pressure injury development (OR = 0.12, 95% CI: 0.03, 0.56, P = 0.007). The use of a PRFM also contributed to a postponed occurrence of pressure injuries by 4.2 days on average in comparison with that of a NPRFM (P = 0.041). CONCLUSIONS: A PRFM is associated with a significantly reduced incidence and postponed occurrence of pressure injuries. It is recommended to use a PRFM for patients at risk of developing pressure injuries.

Development of a telehealthcare decision support system for patients discharged from the hospital.

OBJECTIVE: This article presents the development of a telehealthcare decision support system (TDSS) for patients discharged from the hospital, where symptom data are important indications of the recovery progress for patients. Symptom data are difficult to quantify in a telehealthcare application scenario because the observations and perceptions on symptoms by the patient themselves are subjective. In the TDSS, both symptom data from patients and clinical histories from the hospital information system are collected. Machine learning algorithms are used to build a predictive model for classifying patients according to their symptom data and clinical histories, to provide a degree of urgency for the patient to return to the hospital. MATERIALS AND METHODS: During a 1-year period, 1,467 patient cases were collected. Symptom data and clinical histories were preprocessed into 49 parameters for machine learning. The training data of patients were validated manually with their actual clinical histories of returning to the hospital. The performances of predictive models trained by five different machine learning algorithms were evaluated and compared. RESULTS: The Bayesian network algorithm had the best performance among the machine learning algorithms tested in this application scenario and was selected to be implemented in the TDSS. On the 1,467 patient cases collected, its precision in 10-fold cross-validation was 79.3%. The most important six parameters were also selected from the 49 parameters by feature selection. The performance of correct prediction by the TDSS is comparable to that by the nursing team at the call center. CONCLUSIONS: The TDSS provides a degree of urgency for patients to return to the hospital and thereby assists the telehealthcare nursing team in making such decisions. The performance of the TDSS is expected to improve as more cases of patient data are collected and input into the TDSS. The TDSS has been implemented in one of the largest commercialized telehealthcare practices in Taiwan administered by Min-Sheng General Hospital.

Development and practice of a Telehealthcare Expert System (TES).

OBJECTIVE: Expert systems have been widely used in medical and healthcare practice for various purposes. In addition to vital sign data, important concerns in telehealthcare include the compliance with the measurement prescription, the accuracy of vital sign measurements, and the functioning of vital sign meters and home gateways. However, few expert system applications are found in the telehealthcare domain to address these issues. MATERIALS AND METHODS: This article presents an expert system application for one of the largest commercialized telehealthcare practices in Taiwan by Min-Sheng General Hospital. The main function of the Telehealthcare Expert System (TES) developed in this research is to detect and classify events based on the measurement data transmitted to the database at the call center, including abnormality of vital signs, violation of vital sign measurement prescriptions, and malfunction of hardware devices (home gateway and vital sign meter). When the expert system detects an abnormal event, it assigns an "urgent degree" and alerts the nursing team in the call center to take action, such as phoning the patient for counseling or to urge the patient to return to the hospital for further tests. RESULTS: During 2 years of clinical practice, from 2009 to 2011, 19,182 patients were served by the expert system. The expert system detected 41,755 events, of which 22.9% indicated abnormality of vital signs, 75.2% indicated violation of measurement prescription, and 1.9% indicated malfunction of devices. On average, the expert system reduced by 76.5% the time that the nursing team in the call center spent in handling the events. CONCLUSIONS: The expert system helped to reduce cost and improve quality of the telehealthcare service.

Using n-gram analysis to cluster heartbeat signals.

BACKGROUND: Biological signals may carry specific characteristics that reflect basic dynamics of the body. In particular, heart beat signals carry specific signatures that are related to human physiologic mechanisms. In recent years, many researchers have shown that representations which used non-linear symbolic sequences can often reveal much hidden dynamic information. This kind of symbolization proved to be useful for predicting life-threatening cardiac diseases. METHODS: This paper presents an improved method called the "Adaptive Interbeat Interval Analysis (AIIA) method". The AIIA method uses the Simple K-Means algorithm for symbolization, which offers a new way to represent subtle variations between two interbeat intervals without human intervention. After symbolization, it uses the n-gram algorithm to generate different kinds of symbolic sequences. Each symbolic sequence stands for a variation phase. Finally, the symbolic sequences are categorized by classic classifiers. RESULTS: In the experiments presented in this paper, AIIA method achieved 91% (3-gram, 26 clusters) accuracy in successfully classifying between the patients with Atrial Fibrillation (AF), Congestive Heart Failure (CHF) and healthy people. It also achieved 87% (3-gram, 26 clusters) accuracy in classifying the patients with apnea. CONCLUSIONS: The two experiments presented in this paper demonstrate that AIIA method can categorize different heart diseases. Both experiments acquired the best category results when using the Bayesian Network. For future work, the concept of the AIIA method can be extended to the categorization of other physiological signals. More features can be added to improve the accuracy.

Real-time gait cycle parameter recognition using a wearable accelerometry system.

This paper presents the development of a wearable accelerometry system for real-time gait cycle parameter recognition. Using a tri-axial accelerometer, the wearable motion detector is a single waist-mounted device to measure trunk accelerations during walking. Several gait cycle parameters, including cadence, step regularity, stride regularity and step symmetry can be estimated in real-time by using autocorrelation procedure. For validation purposes, five Parkinson's disease (PD) patients and five young healthy adults were recruited in an experiment. The gait cycle parameters among the two subject groups of different mobility can be quantified and distinguished by the system. Practical considerations and limitations for implementing the autocorrelation procedure in such a real-time system are also discussed. This study can be extended to the future attempts in real-time detection of disabling gaits, such as festinating or freezing of gait in PD patients. Ambulatory rehabilitation, gait assessment and personal telecare for people with gait disorders are also possible applications.

A review of accelerometry-based wearable motion detectors for physical activity monitoring.

Characteristics of physical activity are indicative of one's mobility level, latent chronic diseases and aging process. Accelerometers have been widely accepted as useful and practical sensors for wearable devices to measure and assess physical activity. This paper reviews the development of wearable accelerometry-based motion detectors. The principle of accelerometry measurement, sensor properties and sensor placements are first introduced. Various research using accelerometry-based wearable motion detectors for physical activity monitoring and assessment, including posture and movement classification, estimation of energy expenditure, fall detection and balance control evaluation, are also reviewed. Finally this paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies.

Development of a wearable motion detector for telemonitoring and real-time identification of physical activity.

Characteristics of physical activity are indicative of one's mobility level, latent chronic diseases, and aging process. Current research has been oriented to provide quantitative assessment of physical activity with ambulatory monitoring approaches. This study presents the design of a portable microprocessor-based accelerometry measuring device to implement real-time physical activity identification. An algorithm was developed to process real-time tri-axial acceleration signals produced by human movement to identify targeted still postures, postural transitions, and dynamic movements. Fall detection was also featured in this algorithm to meet the increasing needs of elderly care in free-living environments. High identification accuracy was obtained in performance evaluation. This device is technically viable for telemonitoring and real-time identification of physical activity, while providing sufficient information to evaluate a person's activity of daily living and her/his status of physical mobility. Limitations regarding real-time processing and implementation of the system for telemonitoring in the home environment were also observed.

Development of a portable device for telemonitoring of snoring and obstructive sleep apnea syndrome symptoms.

The First Intensive Balkan Telemedicine and e-Health Seminar was held in the war-ravaged capital of Kosova, Prishtina, in 2002. This event was created under the auspices of the International Virtual e Hospital (IVeH). This organization, the brain child of Rifat Latifi, M.D., was established with the intent to establish a medical capability using telemedicine in an environment where the health infrastructure had been totally destroyed by war. Recently, the IVeH opened six regional telemedicine centers in Kosova. These centers are in the cities of Gjilan, Prizren, Gjakove, Peja, Skenderaj, and Mitrovica. These centers cover the entire country through a telemedicine network. Recently, the Second Intensive Seminar was organized and held October 2123, 2007, in Tirana, Albania. It was organized to broaden the concept of telemedicine and e-health in the Balkans region, and to introduce telemedicine in Albania, which was an ideal choice for holding the seminar. The seminar represented a significant step for Albania as it embraces the concept of telemedicine. These important events have shaped telemedicine development in the Balkans and are serving as a model for the rest of the South Eastern European countries to embrace telemedicine and e-health. This paper summarizes the events of this second seminar and addresses the importance telemedicine has for the region.

Development of a portable device for telemonitoring of physical activities during sleep.

Low motor activity levels and prolonged episodes of uninterrupted immobility are characteristics of sleep. In clinical practice, the use of polysomnographic (PSG) recording is a standard procedure to assess sleep. However, PSG is not suitable for long-term monitoring in the home environment. This paper describes the development of a portable telemonitoring device that detects movements of a subject by conductive mats, and evaluates sleep stages via physical activity data. The device itself also serves as a Web server. Doctors and caregivers can access real-time and historical data via an IE browser or a remote application program for telemonitoring of physical activities and sleep/awake states during sleep, while the patients stay in their own homes. In our validation test with four normal subjects and four arousal subjects, this system showed a good performance in locating sleep epochs of a subject. The sensitivity of locating sleep epochs was 89.5% and the average positive prediction value was 94.8%, with a specificity of 84.3%. This device is not intended to be a diagnosis device, instead, it is to be used as a home telehealth tool for monitoring physical activity and sleep/awake states. This portable telemonitoring device provides a convenient approach to better understand and recognize a subject's sleep pattern through long-term sleep monitoring in the home environment.

Developing a telepresence robot for interpersonal communication with the elderly in a home environment.

"Telepresence" is an interesting field that includes virtual reality implementations with human-system interfaces, communication technologies, and robotics. This paper describes the development of a telepresence robot called Telepresence Robot for Interpersonal Communication (TRIC) for the purpose of interpersonal communication with the elderly in a home environment. The main aim behind TRIC's development is to allow elderly populations to remain in their home environments, while loved ones and caregivers are able to maintain a higher level of communication and monitoring than via traditional methods. TRIC aims to be a low-cost, lightweight robot, which can be easily implemented in the home environment. Under this goal, decisions on the design elements included are discussed. In particular, the implementation of key autonomous behaviors in TRIC to increase the user's capability of projection of self and operation of the telepresence robot, in addition to increasing the interactive capability of the participant as a dialogist are emphasized. The technical development and integration of the modules in TRIC, as well as human factors considerations are then described. Preliminary functional tests show that new users were able to effectively navigate TRIC and easily locate visual targets. Finally the future developments of TRIC, especially the possibility of using TRIC for home tele-health monitoring and tele-homecare visits are discussed.

Development of a decentralized telehomecare monitoring system.

Although the usefulness of telehomecare has been recognized, and all technologies required are readily available, expectations for its widespread adoption have not been realized. This paper presents the development of a portable telehomecare monitoring system (PTMS). What sets this work apart from most other systems is the focus on a highly decentralized monitoring model and the portable nature of the system. We believe that this is the approach that is needed to make such systems economically viable and acceptable to the end-users. The PTMS is a decentralized system, in which a single household is the fundamental unit for sensing, data transmission, data storage and analysis. It is not necessary to subscribe service from a health welfare center, and the infrastructure required is minimal, too. Equipped with different sensors, the PTMS can be used for long-term personal health data management in a home environment, and it can be easily implemented in a home environment at very low cost. Similar to the largescale telehomecare systems, the PTMS also provides caregivers with convenient access to the health data and real-time event-driven messages in urgent situations. Several PTMS applications are described in this paper, including environmental monitoring, monitoring of activities of daily living, a radiofrequency identification (RFID)-based entrance guard system, sleep quality monitoring, vital sign parameter monitoring, and a tele-presence robot.