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This paper describes a method for the estimation of the 3D ground reaction force (GRF) during human walking using novel nanocomposite piezo-responsive foam (NCPF) sensors. Nine subjects (5 male, 4 female) walked on a force-instrumented treadmill at 1.34 m/s for 120 s each while wearing a shoe that was instrumented with four NCPF sensors. GRF data, measured via the treadmill, and sensor data, measured via the NCPF inserts, were used in a tenfold cross validation process to calibrate a separate model for each individual. The calibration model estimated average anterior-posterior, mediolateral and vertical GRF with mean average errors (MAE) of 6.52 N (2.14%), 4.79 N (6.34%), and 15.4 N (2.15%), respectively. Two additional models were created using the sensor data from all subjects and subject demographics. A tenfold cross validation process for this combined data set resulted in models that estimated average anterior-posterior, mediolateral and vertical GRF with less than 8.16 N (2.41%), 6.63 N (7.37%), and 19.4 N (2.31%) errors, respectively. Intra-subject estimates based on the model had a higher accuracy than inter-subject estimates, likely due to the relatively small subject cohort used in creating the model. The novel NCPF sensors demonstrate the ability to accurately estimate 3D GRF during human movement outside of the traditional biomechanics laboratory setting.

It is believed by many neurosurgeons that in addition to age and neurological status, the CT patterns of traumatic intracerebral haemorrhages are related to outcome. The aim of this study was to find out whether this is the case. The study was conducted in a regional level I trauma centre in Hong Kong. We prospectively collected data of patients with traumatic intracerebral haematomas over a 4-year period. Of 464 patients with head injuries, traumatic intracerebral haematoma was significantly associated with inpatient mortality and one year unfavorable outcome after adjustment for age, sex, post-resuscitation GCS, and presence of acute subdural haematoma. One hundred-and-fourteen patients had traumatic intracerebral haematomas and were included for further analysis. The mean age was 49, the male to female ratio was 2 to 1, and the median Glasgow Coma Scale (GCS) score on admission was 12. Logistic regression analysis showed that age and GCS score/GCS motor component score were significant factors for inpatient mortality, one-year mortality and one-year outcome. There was an association between temporal haematomas and inpatient mortality, subdural haematomas and inpatient mortality, and bilateral haematomas and unfavourable one-year outcome. In patients with severe head injury, a traumatic haematoma of more than 50 ml was associated higher inpatient mortality. In addition to age and GCS score, the CT patterns of bilateral haematomas, temporal haematomas and associated subdural haematomas were suggestive of poor outcome or mortality.

Background: Physical activity and corresponding energy expenditure can improve health in various ways. Existing methods to directly measure energy expenditure are currently limited to laboratory settings and/or expensive instrumentation. The purpose of this study was to evaluate accuracy of energy expenditure characterization, during walking and running, using demographic data, as well as data collected via an accelerometer and novel piezoresponsive foam sensors. Methods: 30 individuals (14 females; mass = 67 ± 10 kg; height = 1.74 ± 0.08 m; age = 23 ± 3 yrs) walked and ran at five speeds (1.34, 2.23, 2.68, 3.13, and 3.58 m/s) on a force-instrumented treadmill while wearing a metabolic analyzer and standardized athletic shoes instrumented with an accelerometer, and four novel nanocomposite piezoresponsive force sensors. Various predictive models, including demographic data and data derived from the accelerometer and force sensors, were evaluated for each gait speed. Results: The predictive models varied in ability to accurately characterize energy expenditure. For walking, the most accurate model included acceleration and body weight, and resulted in an average absolute error of 0.07 ± 0.03 kcal/min. For running, the most accurate model included sensor and acceleration data, and resulted in an average absolute error of 0.45 ± 0.14 kcal/min. Conclusions: When combined with acceleration data and body weight, the novel foam sensors can be used to inexpensively and accurately measure walking and running energy expenditure. This can be done at various speeds, outside of a traditional research laboratory. These results have application within a wide range of diverse contexts.