Development of Transfer Assist Robot Based on the User Needs

To develop technology based on the need for a Roboticbed® by the elderly and those with disabilities, this study extracts user needs and problems based on feasibility testing. This study makes it clear that those with severe disabilities want the Roboticbed® to support their independence in moving between bed and wheelchair and back. We propose a lifestyle for users and caregivers using the present version of the Roboticbed® based on experiments and extracted problems based on user needs. These results indicate that evaluation based on user needs is useful for specifying a design guide for user needs.首都大学東京学位論文甲第476号 副論文 (要約

使用者中心の福祉機器臨床評価手法 : プロトタイプロボティックベッドの評価

首都大学東京, 2015-03-25, 博士（作業療法学）, 甲第476号首都大学東

Prediction of stroke patients’ bedroom-stay duration: machine-learning approach using wearable sensor data

Background: The importance of being physically active and avoiding staying in bed has been recognized in stroke rehabilitation. However, studies have pointed out that stroke patients admitted to rehabilitation units often spend most of their day immobile and inactive, with limited opportunities for activity outside their bedrooms. To address this issue, it is necessary to record the duration of stroke patients staying in their bedrooms, but it is impractical for medical providers to do this manually during their daily work of providing care. Although an automated approach using wearable devices and access points is more practical, implementing these access points into medical facilities is costly. However, when combined with machine learning, predicting the duration of stroke patients staying in their bedrooms is possible with reduced cost. We assessed using machine learning to estimate bedroom-stay duration using activity data recorded with wearable devices.Method: We recruited 99 stroke hemiparesis inpatients and conducted 343 measurements. Data on electrocardiograms and chest acceleration were measured using a wearable device, and the location name of the access point that detected the signal of the device was recorded. We first investigated the correlation between bedroom-stay duration measured from the access point as the objective variable and activity data measured with a wearable device and demographic information as explanatory variables. To evaluate the duration predictability, we then compared machine-learning models commonly used in medical studies.Results: We conducted 228 measurements that surpassed a 90% data-acquisition rate using Bluetooth Low Energy. Among the explanatory variables, the period spent reclining and sitting/standing were correlated with bedroom-stay duration (Spearman’s rank correlation coefficient (R) of 0.56 and −0.52, p < 0.001). Interestingly, the sum of the motor and cognitive categories of the functional independence measure, clinical indicators of the abilities of stroke patients, lacked correlation. The correlation between the actual bedroom-stay duration and predicted one using machine-learning models resulted in an R of 0.72 and p < 0.001, suggesting the possibility of predicting bedroom-stay duration from activity data and demographics.Conclusion: Wearable devices, coupled with machine learning, can predict the duration of patients staying in their bedrooms. Once trained, the machine-learning model can predict without continuously tracking the actual location, enabling more cost-effective and privacy-centric future measurements

Selective β-Arylation of Thiophenes with Aryl Iodides Catalyzed by Dinuclear Palladium Carboxylate Complexes

autho

Highlighting Unseen Activity Through 48-Hour Continuous Measurement in Subacute Stroke Rehabilitation: Preliminary Cohort Study

BackgroundMotor impairments not only lead to a significant reduction in patient activity levels but also trigger a further deterioration in motor function due to deconditioning, which is an issue that is particularly pronounced during hospitalization. This deconditioning can be countered by sustaining appropriate activity levels. Activities that occur outside of scheduled programs, often overlooked, are critical in this context. Wearable technology, such as smart clothing, provides a means to monitor these activities. ObjectiveThis study aimed to observe activity levels in patients who had strokes during the subacute phase, focusing on both scheduled training sessions and other nontraining times in an inpatient rehabilitation environment. A smart clothing system is used to simultaneously measure heart rate and acceleration, offering insights into both the amount and intensity of the physical activity. MethodsIn this preliminary cohort study, 11 individuals undergoing subacute stroke rehabilitation were enrolled. The 48-hour continuous measurement system, deployed at admission and reassessed 4 weeks later, monitored accelerometry data for physical activity (quantified with a moving SD of acceleration [MSDA]) and heart rate for intensity (quantified with percent heart rate reserve). The measurements were performed using a wearable activity monitoring system, the hitoe (NTT Corporation and Toray Industries, Inc) system comprising a measuring garment (wear or strap) with integrated electrodes, a data transmitter, and a smartphone. The Functional Independence Measure was used to assess the patients’ daily activity levels. This study explored factors such as differences in activity during training and nontraining periods, correlations with activities of daily living (ADLs) and age, and changes observed after 4 weeks. ResultsA significant increase was found in the daily total MSDA after the 4-week program, with the average percent heart rate reserve remaining consistent. Physical activity during training positively correlated with ADL levels both at admission (ρ=0.86, P<.001) and 4 weeks post admission (ρ=0.96, P<.001), whereas the correlation between age and MSDA was not significant during training periods at admission (ρ=–0.41, P=.21) or 4 weeks post admission (ρ=–0.25, P=.45). Conversely, nontraining activity showed a negative correlation with age, with significant negative correlations with age at admission (ρ=–0.82, P=.002) and 4 weeks post admission (ρ=–0.73, P=.01). ConclusionsInpatient rehabilitation activity levels were positively correlated with ADL levels. Further analysis revealed a strong positive correlation between scheduled training activities and ADL levels, whereas nontraining activities showed no such correlation. Instead, a negative correlation between nontraining activities and age was observed. These observations suggest the importance of providing activity opportunities for older patients, while it may also suggest the need for adjusting the activity amount to accommodate the potentially limited fitness levels of this demographic. Future studies with larger patient groups are warranted to validate and further elucidate these findings

Lysophosphatidic Acid Receptor 4 Activation Augments Drug Delivery in Tumors by Tightening Endothelial Cell-Cell Contact

Vascular normalization in tumors may improve drug delivery and anti-tumor immunity. Angiogenesis inhibitors induce hypoxia, which may facilitate malignant progression; therefore, we investigated other methods to promote vascular maturation. Here, we show that lysophosphatidic acid (LPA) enhances blood flow by promoting fine vascular networks, thereby improving vascular permeability and suppressing tumor growth when combined with anti-cancer drug treatment. Six different G protein-coupled receptors have been identified as LPA receptors (LPA1–6). In studies using mutant mice, we found that LPA4 is involved in vascular network formation. LPA4 activation induces circumferential actin bundling beneath the cell membrane and enhances linear adherens junction formation by VE-cadherin in endothelial cells. Therefore, we conclude that activation of LPA4 is a promising approach for vascular regulation