BACKGROUND Long-term care (LTC) homes face the challenges of increasing care needs of residents and a shortage of health care providers. Literature suggests that artificial intelligence (AI)–enabled robots may solve such challenges and support person-centered care. There is a dearth of literature exploring the perspectives of health care providers, which are crucial to implementing AI-enabled robots. OBJECTIVE This scoping review aims to explore this scant body of literature to answer two questions: (1) what barriers do health care providers perceive in adopting AI-enabled robots in LTC homes? (2) What strategies can be taken to overcome these barriers to the adoption of AI-enabled robots in LTC homes? METHODS We are a team consisting of 3 researchers, 2 health care providers, 2 research trainees, and 1 older adult partner with diverse disciplines in nursing, social work, engineering, and medicine. Referring to the Joanna Briggs Institute methodology, our team searched databases (CINAHL, MEDLINE, PsycINFO, Web of Science, ProQuest, and Google Scholar) for peer-reviewed and gray literature, screened the literature, and extracted the data. We analyzed the data as a team. We compared our findings with the Person-Centered Practice Framework and Consolidated Framework for Implementation Research to further our understanding of the findings. RESULTS This review includes 33 articles that met the inclusion criteria. We identified three barriers to AI-enabled robot adoption: (1) perceived technical complexity and limitation; (2) negative impact, doubted usefulness, and ethical concerns; and (3) resource limitations. Strategies to mitigate these barriers were also explored: (1) accommodate the various needs of residents and health care providers, (2) increase the understanding of the benefits of using robots, (3) review and overcome the safety issues, and (4) boost interest in the use of robots and provide training. CONCLUSIONS Previous literature suggested using AI-enabled robots to resolve the challenges of increasing care needs and staff shortages in LTC. Yet, our findings show that health care providers might not use robots because of different considerations. The implication is that the voices of health care providers need to be included in using robots. INTERNATIONAL REGISTERED REPORT RR2-doi:10.1136/bmjopen-2023-075278
An automatic document classification system is useful to manage the massive quantities of documents such as the Web document collection. However, its complicated process of classification has become a serious problem when applying it to general services. In this paper, we suggest an efficient data structure for the document classification and develop a classification system based on a trie-based index structure. This efficient data structure reduces overheads for the task of document classification using naive Bayesian probabilistic models and makes it possible to implement commercial applications. In our system, both learning and classification are performed in a Web-based user interface rather than by a remote application, which contributes to achieve easy control of the classification process and the flexibility of diverse document provision.
Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets.
Telecardiology provides mobility for patients who require constant electrocardiogram (ECG) monitoring. However, its safety is dependent on the predictability and robustness of data delivery, which must overcome errors in the wireless channel through which the ECG data are transmitted. We report here a framework that can be used to gauge the applicability of IEEE 802.11 wireless local area network (WLAN) technology to ECG monitoring systems in terms of delay constraints and transmission reliability. For this purpose, a medical-grade WLAN architecture achieved predictable delay through the combination of a medium access control mechanism based on the point coordination function provided by IEEE 802.11 and an error control scheme based on Reed-Solomon coding and block interleaving. The size of the jitter buffer needed was determined by this architecture to avoid service dropout caused by buffer underrun, through analysis of variations in transmission delay. Finally, we assessed this architecture in terms of service latency and reliability by modeling the transmission of uncompressed two-lead electrocardiogram data from the MIT-BIH Arrhythmia Database and highlight the applicability of this wireless technology to telecardiology.
This paper proposes robotic technology component with UPnP (Universal Plug and Play) communication. This research uses RTC (robotic technology component) specification that has been standardized by object management group (OMG). The modularity of robot component lets the robot developer to design their own interface communication. UPnP as communication technology offers pervasive peer-to-peer network connectivity of intelligent appliances in dynamic distributed computing environment. UPnP technology has good features for this environment and gives benefits when developing robots for ubiquitous environment. This paper shows a method to integrate the RTC and UPnP. It shows that UPnP technology can provide communication between RT Components. This paper also presents a simulation result of applying UPnP technology to RTC.
In this paper, we will show that the status certificate-based encryption scheme proposed by Yum and Lee is insecure against key substitution attacks by two types of attackers.
A variant of the self-shrinking generator (SSG) proposed at ICISC 2006, which we call SSG-XOR, was claimed to have better cryptographic properties than SSG in a practical setting. It was also claimed that SSG-XOR will be more secure than SSG. But we show that SSG-XOR has no advantage over SSG from the viewpoint of practical cryptanalysis, especially the guess-and-determine attack.
IP multicast shows a very strong advantage in the group communication services. To deploy IP multicast in the current Internet, every IP router must be changed into multicast enabled ones and several deployment issues should be solved. These problems prevent IP multicast mechanism from being deployed in the IP world. These problems gives a good reason why today's group communication uses replicated IP unicast mechanism instead of IP multicast. Recently an overlay multicast is proposed as an alternative method of IP multicast. We propose a RMCP (relayed multicast protocol) as an application-layer protocol for providing end-to-end multicast services over IP-network environment. We also push RMCP into ITU-T and ISO standardization activity. In this paper we define the basic concepts of relayed multicast scheme, data delivery models, service scenarios and standardization activities on RMCP
Abstract Chronic pain is a major public health problem affecting approximately 100 million Americans and United States military Veterans, who constitute a particularly vulnerable group. While pain research in Veterans is actively underway, information on the longitudinal course of pain in this population is limited. This study aimed to 1) identify the various longitudinal pain status trajectories among older Veterans over a 10-year period and 2) detect factors predicting membership in the worsening trajectory of chronic pain. We analyzed data from 619 Veterans (mean age: 58.5 years) participating in the Mind Your Heart Study, an ongoing prospective cohort study examining diverse health outcomes among Veterans. Initially, we employed a generalized mixture model to identify pain trajectory classes using Brief Pain Inventory (BPI) pain intensity subscale score collected at 2-, 5-, and 10-year intervals. Two distinct trajectories were identified—low and high—both of which remained relatively stable. Subsequently, several feature selection methods extracted the predominant features from participants’ baseline characteristics that predicted membership in the high vs. low pain trajectory. These included: prior arthritis diagnosis; prior post-traumatic stress disorder (PTSD) diagnosis; depression symptoms; PTSD symptoms of avoidance, hyperarousal, and negative mood alterations; physical functioning; sleep quality; and overall health. The scikit-learn RandomForestClassifier, utilizing the refined feature set, achieved a classification accuracy of 0.79, yielding results nearly identical to those obtained using all 261 features. These findings are clinically informative and pertinent, highlighting potential intervention targets warranting intensive pain care plans based on probable long-term prognosis and discussing early treatment strategies among older Veterans.
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