Now showing 1 - 5 of 5
  • Publication
    Sensor-based Assessment of Falls Risk of the Timed Up and Go in Real-World Settings
    Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings.
  • Publication
    Friction perception at initial contact with textured and rough surfaces
    For safe and dexterous manipulation humans need frictional information to adjust and control grip forces precisely. This study indicates that humans cannot perceive frictional difference at initial contact with passive touch on either smooth surface or textured and rough surfaces. This is true even when tangential force is present.
  • Publication
    Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors
    Falls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the “first fall”, delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.
      313Scopus© Citations 29
  • Publication
    Estimating Lower Limb Kinematics using Distance Measurements with a Reduced Wearable Inertial Sensor Count
    This paper presents an algorithm that makes novel use of distance measurements alongside a constrained Kalman filter to accurately estimate pelvis, thigh, and shank kinematics for both legs during walking and other body movements using only three wearable inertial measurement units (IMUs). The distance measurement formulation also assumes hinge knee joint and constant body segment length, helping produce estimates that are near or in the constraint space for better estimator stability. Simulated experiments have shown that inter-IMU distance measurement is indeed a promising new source of information to improve the pose estimation of inertial motion capture systems under a reduced sensor count configuration. Furthermore, experiments show that performance improved dramatically for dynamic movements even at high noise levels (e.g., σ dist = 0.2 m), and that acceptable performance for normal walking was achieved at σ dist = 0.1 m. Nevertheless, further validation is recommended using actual distance measurement sensors.
      17Scopus© Citations 5
  • Publication
    Estimating Lower Limb Kinematics using a Lie Group Constrained EKF and a Reduced Wearable IMU Count
    This paper presents a novel algorithm using Lie group representation of position and orientation alongside a constrained extended Kalman filter (CEKF) to accurately estimate pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. The algorithm iterates through the prediction update (kinematic equation), measurement update (pelvis height, zero velocity update, flatfloor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The paper also describes a novel Lie group formulation of the assumptions implemented in the said measurement and constraint updates. Evaluation of the algorithm on nine healthy subjects who walked freely within a 4 x 4m 2 room shows that the knee and hip joint angle root-mean-square errors (RMSEs) in the sagittal plane for free walking were 10.5±2.8° and 9.7 ± 3.3°, respectively, while the correlation coefficients (CCs) were 0.89 ± 0.06 and 0.78 ± 0.09, respectively. The evaluation demonstrates a promising application of Lie group representation to inertial motion capture under reduced-sensorcount configuration, improving the estimates (i.e., joint angle RMSEs and CCs) for dynamic motion, and enabling better convergence for our non-linear biomechanical constraints. To further improve performance, additional information relating the pelvis and ankle kinematics is needed.
      18Scopus© Citations 5