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Importance: Stroke is the leading cause of long-term disability in the United States. Providers have no robust tools to objectively and accurately measure the activity of people with stroke living at home. Objective: To explore the integration of validated upper extremity assessments poststroke within an activity recognition system. Design: Exploratory descriptive study using data previously collected over 3 mo to report on algorithm testing and assessment integration. Setting: Data were collected in the homes of community-dwelling participants. Participants: Participants were at least 6 mo poststroke, were able to ambulate with or without an assistive device, and self-reported some difficulty using their arm in everyday activities. Outcomes and Measures: The activity detection algorithm's accuracy was determined by comparing its activity labels with manual labels. The algorithm integrated assessment by describing the quality of upper extremity movement, which was determined by reporting extent of reach, mean and maximum speed during movement, and smoothness of movement. Results: Sixteen participants (9 women, 7 men) took part in this study, with an average age of 63.38 yr (SD = 12.84). The algorithm was highly accurate in correctly identifying activities, with 87% to 95% accuracy depending on the movement. The algorithm was also able to detect the quality of movement for upper extremity movements. Conclusions and Relevance: The algorithm was able to accurately identify in-kitchen activities performed by adults poststroke. Information about the quality of these movements was also successfully calculated. This algorithm has the potential to supplement clinical assessments in treatment planning and outcomes reporting. Plain-Language Summary: This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. The algorithm accurately identified activities that were performed in the kitchen by adults poststroke. The algorithm also identified the quality of upper extremity movements of people poststroke who were living at home. This study shows that clinical algorithms have the potential to inform occupational therapy practice by providing clinically relevant data about the in-home activities of adults poststroke. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For individuals post-stroke, home-based programs are necessary to deliver additional hours of therapy outside of the limited time in the clinic. Virtual reality (VR)-based approaches show modest outcomes in improving client function when delivered in the home. The movement sensors used in these VR-based approaches, such as the Microsoft Kinect® have been validated against gold standards tools but have not been used as an assessment of upper extremity movement quality in the stroke population. The purpose of this study was to explore the use of a movement sensor paired with a VR-based intervention to assess upper extremity movement for individuals post-stroke. Movement data captured with the Microsoft Kinect® from four separate studies were aggregated for analysis (n = 8 individuals post-stroke, n = 30 individuals without disabilities). For all participants, the skeletal data (x, y, z coordinates for 15 tracked joints) for each game play session were processed in MatLab and movement variables (normalized jerk, movement path ratio, average path sway) were calculated using an OPTICS density-based cluster algorithm. Data from the 30 healthy individuals created a normative baseline for the three kinematic variables. Individuals post-stroke were less efficient and had more jerky movements in both upper extremities as compared to healthy individuals. It is feasible to use a movement sensor paired with a VR-based intervention to quantify and qualify upper extremity movement for individuals post-stroke. Further research with a larger cohort is necessary to establish clinical sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Based on the development background of the interweaving and integration of computer technology and Internet technology, China's artificial intelligence industry is quietly rising. In the social life of the information age, the artificial intelligence industry represented by machine deep learning is playing a very important role. This study in combination with the background of the new health reform, in view of the reform of the medical industry, analyzes the connotation of financial wisdom based on the important role of the hospital financial development problems, puts forward the development direction of artificial intelligence hospital financial wisdom development measures, designed to meet the changing external environment demand, reduces human costs, and improves the overall efficiency of hospital financial fund management. Based on the evaluation results, this paper proposes the correct direction for the development of hospital intelligence finance by using the BP neural network model. After the analysis, it is found that the development of artificial intelligence is an important measure to promote the development of hospital intelligent finance. In other words, hospital intelligent financial management is the product of the continuous progress of artificial intelligence technology. At the present stage, the intelligent financial management problems of hospitals are mainly as follows: (1) lack of financial informatization, (2) lack of perfect financial risk early warning system, and (3) the phenomenon of "information island" in the financial system. After analyzing the above problems, the research believes that the development of hospital intelligent finance based on artificial intelligence needs to solve the above thorny problems, so as to improve the outcome of hospital intelligent finance development. The following work should be done: (1) strengthen the design of information sharing module, (2) intelligent control of the cost of hospitals, and (3) intelligent treatment of hospital accounting. Combining the development of artificial intelligence and hospital intelligent finance theory, combined with the actual trend of financial intelligence development under the background of artificial intelligence development in the new era, it provides scientific basis for the development of hospital intelligent finance. [ABSTRACT FROM AUTHOR]

Interactive technologies are beneficial to stroke recovery as rehabilitation interventions; however, they lack evidence for use as assessment tools. Mystic Isle is a multi-planar full-body rehabilitation game developed using the Microsoft Kinect® V2. It aims to help stroke patients improve their motor function and daily activity performance and to assess the motions of the players. It is important that the assessment results generated from Mystic Isle are accurate. The Kinect V2 has been validated for tracking lower limbs and calculating gait-specific parameters. However, few studies have validated the accuracy of the Kinect® V2 skeleton model in upper-body movements. In this paper, we evaluated the spatial accuracy and measurement validity of a Kinect-based game Mystic Isle in comparison to a gold-standard optical motion capture system, the Vicon system. Thirty participants completed six trials in sitting and standing. Game data from the Kinect sensor and the Vicon system were recorded simultaneously, then filtered and sample rate synchronized. The spatial accuracy was evaluated using Pearson’s r correlation coefficient, signal to noise ratio (SNR) and 3D distance difference. Each arm-joint signal had an average correlation coefficient above 0.9 and a SNR above 5. The hip joints data had less stability and a large variation in SNR. Also, the mean 3D distance difference of joints were less than 10 centimeters. For measurement validity, the accuracy was evaluated using mean and standard error of the difference, percentage error, Pearson’s r correlation coefficient and intra-class correlation (ICC). Average errors of maximum hand extent of reach were less than 5% and the average errors of mean and maximum velocities were about 10% and less than 5%, respectively. We have demonstrated that Mystic Isle provides accurate measurement and assessment of movement relative to the Vicon system. [ABSTRACT FROM AUTHOR]

Date Presented 03/27/20 Current assessments of activity poststroke occur in clinical settings. Researchers have attempted to use wearable activity monitors to assess activity in natural environments, but they are unable to distinguish between activities. We have developed an algorithm within a depth sensor that is able to distinguish between activities that occur within a kitchen setting. The purpose of this study was to test the system with individuals poststroke in an unstructured home environment. Primary Author and Speaker: Rachel Proffitt Contributing Authors: Mengxuan Ma, Marjorie Skubic [ABSTRACT FROM AUTHOR]

Date Presented 03/26/20 This research provides an exploration into the use of virtual reality–based tools for assessment in stroke. As these tools become more widely used in practice, it is imperative that OT practitioners are able to utilize data effectively in clinical decision-making. Primary Author and Speaker: Rachel Proffitt Contributing Authors: Mengxuan Ma, Marjorie Skubic [ABSTRACT FROM AUTHOR]