Virtual Hand Illusion in younger and older adults.

INTRODUCTION: Embodiment involves experiencing ownership over our body and localizing it in space and is informed by multiple senses (visual, proprioceptive and tactile). Evidence suggests that embodiment and multisensory integration may change with older age. The Virtual Hand Illusion (VHI) has been used to investigate multisensory contributions to embodiment, but has never been evaluated in older adults. Spatio-temporal factors unique to virtual environments may differentially affect the embodied perceptions of older and younger adults. METHODS: Twenty-one younger (18-35 years) and 19 older (65+ years) adults completed the VHI paradigm. Body localization was measured at baseline and again, with subjective ownership ratings, following synchronous and asynchronous visual-tactile interactions. RESULTS: Higher ownership ratings were observed in the synchronous relative to the asynchronous condition, but no effects on localization/drift were found. No age differences were observed. Localization accuracy was biased in both age groups when the virtual hand was aligned with the real hand, indicating a visual mislocalization of the virtual hand. CONCLUSIONS: No age-related differences in the VHI were observed. Mislocalization of the hand in VR occurred for both groups, even when congruent and aligned; however, tactile feedback reduced localization biases. Our results expand the current understanding of age-related changes in multisensory embodiment within virtual environments.

Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses' Perceptions and Lessons Learned.

ABSTRACTHospitalized older adults are at high risk of falling. The HELPER system is a ceiling-mounted fall detection system that sends an alert to a smartphone when a fall is detected. This article describes the performance of the HELPER system, which was pilot tested in a geriatric mental health hospital. The system's accuracy in detecting falls was measured against the hospital records documenting falls. Following the pilot test, nurses were interviewed regarding their perceptions of this technology. In this study, the HELPER system missed one documented fall but detected four falls that were not documented. Although sensitivity (.80) of the system was high, numerous false alarms brought down positive predictive value (.01). Interviews with nurses provided valuable insights based on the operation of the technology in a real environment; these and other lessons learned will be particularly valuable to engineers developing this and other health and social care technologies.

Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery.

INTRODUCTION: Measurements from upper limb rehabilitation robots could guide therapy progression, if a robotic assessment's measurement error was small enough to detect changes occurring on a time scale of a few days. To guide this determination, this study evaluated the smallest real differences of robotic measures, and of clinical outcome assessments predicted from these measures. METHODS: A total of nine older chronic stroke survivors took part in 12-week study with an upper-limb end-effector robot. Fourteen robotic measures were extracted, and used to predict Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT) scores using multilinear regression. Smallest real differences and intraclass correlation coefficients were computed for the robotic measures and predicted clinical outcomes, using data from seven baseline sessions. RESULTS: Smallest real differences of robotic measures ranged from 8.8% to 26.9% of the available range. Smallest real differences of predicted clinical assessments varied widely depending on the regression model (1.3 to 36.2 for FMA-UE, 1.8 to 59.7 for ARAT), and were not strongly related to a model's predictive performance or to the smallest real differences of the model inputs. Models with acceptable predictive performance as well as low smallest real differences were identified. CONCLUSIONS: Smallest real difference evaluations suggest that using robotic assessments to guide therapy progression is feasible.