Kinematic analysis of preparation for transferring from wheelchair to bed.

This study aimed to clarify the kinematics, particularly of the shoulder and hip joints, during preparation for manual wheelchair-to-bed transfer (i.e. when flipping up the arm and foot supports). This cross-sectional study included 32 able-bodied individuals. The kinematics of the shoulder and hip joints when the arm and foot supports were flipped up of manual wheelchair, were evaluated using a markerless inertial sensor-based motion capture system. We found that flipping the arm support upwards involved a large amount of abduction, internal and external rotation, flexion, and extension at the shoulder joint, whereas flipping the foot support upwards involved a large amount of flexion at the hip joint. The findings suggest that it is necessary to consider the range of motion required to flip up the arm and foot supports of manual wheelchairs, particularly in those with limited shoulder and hip range of motion such as older people, neuromuscular disorders, and orthopedic disorders.

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.

Validity of trunk acceleration measurement with a chest-worn monitor for assessment of physical activity intensity.

BACKGROUND: Recent advancements in wearable technology have enabled easy measurement of daily activities, potentially applicable in rehabilitation practice for various purposes such as maintaining and increasing patients' activity levels. In this study, we aimed to examine the validity of trunk acceleration measurement using a chest monitor embedded in a smart clothing system ('hitoe' system), an emerging wearable system, in assessing the physical activity in an experimental setting with healthy subjects (Study 1) and in a clinical setting with post-stroke patients (Study 2). METHODS: Study 1 involved the participation of 14 healthy individuals. The trunk acceleration, heart rate (HR), and oxygen consumption were simultaneously measured during treadmill testing with a Bruce protocol. Trunk acceleration and HR were measured using the "hitoe" system, a smart clothing system with embedded chest sensors. Expiratory gas analysis was performed to measure oxygen consumption. Three parameters, moving average (MA), moving standard deviation (MSD), and moving root mean square (RMS), were calculated from the norm of the trunk acceleration. The relationships between these accelerometer-based parameters and oxygen consumption-based physical activity intensity measured with the percent VO2 reserve (%VO2R) were examined. In Study 2, 48 h of simultaneous measurement of trunk acceleration and heart rate-based physical activity intensity in terms of percent heart rate reserve (%HRR) was conducted with the "hitoe" system in 136 post-stroke patients. RESULTS: The values of MA, MSD, RMS, and %VO2R were significantly different between levels 1, 2, 3, and 4 in the Bruce protocol (P < 0.01). The average coefficients of determination for individual regression for %VO2R versus MA, %VO2R versus MSD, and %VO2R versus RMS were 0.89 ± 0.05, 0.96 ± 0.03, and 0.91 ± 0.05, respectively. Among the parameters examined, MSD showed the best correlation with %VO2R, indicating high validity of the parameter for assessing physical activity intensity. The 48-h measurement of MSD and %HRR in post-stroke patients showed significant within-individual correlation (P < 0.05) in 131 out of 136 patients (correlation coefficient: 0.60 ± 0.16). CONCLUSIONS: The results support the validity of the MSD calculated from the trunk acceleration measured with a smart clothing system in assessing the physical activity intensity. TRIAL REGISTRATION: UMIN000034967. Registered 21 November 2018 (retrospectively registered).

Novel lateral transfer assist robot decreases the difficulty of transfer in post-stroke hemiparesis patients: a pilot study.

PURPOSE: The purpose of this study was to clarify whether the novel lateral transfer assist robot facilitates easier transfers compared with a wheelchair in post-stroke hemiparesis patients. METHODS: This cross-sectional study enrolled 20 post-stroke hemiparesis patients, and the task difficulty of transfers was compared between a wheelchair and lateral transfer assist robot. All participants were asked to transfer from either wheelchair or lateral transfer assist robot to a platform table and back. The primary outcome was the transfer score of the Functional Independence Measure. The secondary outcome was the time required for transfer. RESULTS: The transfer score of the Functional Independence Measure was significantly higher with lateral transfer assist robot than with wheelchair (p < .001). The transfer times from these devices to a platform table and back showed no significant differences (to device from platform table: 7.8 s, lateral transfer assist robot vs 7.6 s, wheelchair, p > .05: device to platform table: 7.1 s, lateral transfer assist robot vs 8.0 s, wheelchair, p > .05). CONCLUSIONS: Transfer with a lateral transfer assist robot is easier than with wheelchair and facilitates independence in post-stroke hemiparesis patients.IMPLICATIONS FOR REHABILITATIONTransfer skill influences the functional independence and quality of life of a wheelchair userA novel structural mobility device-the lateral transfer assist robot (LTAR)-can facilitate transfersThe LTAR could improve the degree of independence for transfers than the wheelchair, without any time loss, in post-stroke hemiparesis patientsThe LTAR could potentially reduce the risk for falls in various medical and care facilities.

Lateral Transfer Assist Robot (LTAR): Development of a proof-of-concept prototype.

BACKGROUND: Falls during transfer to and from a wheelchair are associated with numerous problems. Factors responsible for difficulty in transferring include horizontal/vertical gaps between surfaces; obstacles, such as armrests; and complicated brake/footrests configurations before transferring. Moreover, controlling a wheelchair sufficiently close to the transfer surface within the confined home space is difficult. OBJECTIVE: We described the design of the novel Lateral Transfer Assist Robot (LTAR) for solving problems during transfer. Furthermore, the effectiveness and usability of the robot were preliminary examined in healthy adults. METHOD: The transfer problems and basic designs were organized. The effectiveness of the prototype was measured by three-dimensional motion analysis and questionnaire. RESULTS: The prototype LTAR was developed. With just a push on a button, the footplate lowers to the floor and the seat and armrest lowers to the height of the seating surface to fill the gap between the surfaces. Using these features, users can transfer by simply shifting their buttocks sideways. Additionally, LTAR has omnidirectional wheels that help move it within a narrow space. The LTAR was confirmed to reduce the physical and subjective burden, except for maneuverability. CONCLUSION: The LTAR was found to be effective for home use and reducing burden of transfer.