Quantitative assessment of ADL: a pilot study of upper extremity reaching tasks

Effective telerehabilitation technologies enable patients with certain physiological disabilities to engage in rehabilitative exercises for performing Activities of Daily Living (ADLs). Therefore, training and assessment scenarios for the performance of ADLs are vital for the promotion for telerehabilitation. In this paper we investigate quantitatively and automatically assessing patient's kinematic ability to perform functional upper extremity reaching tasks. The shape of the movement trajectory and the instantaneous acceleration of kinematically crucial body parts, such as wrists, are used to compute the approximate entropy of the motions to represent stability (smoothness) in addition to the duration of the activity. Computer simulations were conducted to illustrate the consistency, sensitivity and robustness of the proposed method. A preliminary experiment with kinematic data captured from healthy subjects mimicking a reaching task with dyskinesia showed a high degree of correlation (Cohen's kappa 0.85 with p < 0.05) between a human observer and the proposed automatic classification tool in terms of assigning the datasets to various levels to represent the subjects' kinematic abilities to perform reaching tasks. This study supported the use of Microsoft Kinect to quantitatively evaluate the ability of individuals with involuntary movements to perform an upper extremity reaching task.

Changes in gait performance over several years are associated with recurrent falls status in community-dwelling older women at high risk of fracture

Background: gait analysis is a recommended geriatric assessment for falls risk and sarcopenia; however, previous research utilises measurements at a single time point only. It is presently unclear how changes in gait over several years influence risk of recurrent falls in older adults.Methods: we investigated 135 female volunteers (mean age ± SD: 76.7 ± 5.0 years; range: 70-92 years) at high risk of fracture. Gait parameters (speed, cadence, step length, step width, swing time and double support phase) were assessed using the GAITRite Electronic Walkway System at four annual clinics over ?3.7 ± 0.5 years. Participants reported incident falls monthly for 3.7 ± 1.2 years.Results: increasing gait speed (odds ratio: 0.96; 95% confidence interval 0.93, 0.99) and step length (0.87; 0.77, 0.98) from baseline to final follow-up was associated with reduced likelihood of being a recurrent faller over the study period. No significant associations were observed for baseline gait parameters (all P ≥ 0.05). At the second follow-up (2.8 ± 0.6 years), an increase in swing time (0.65; 0.43, 0.98) was associated with reduced likelihood, while an increase in double support phase (1.31; 1.04, 1.66) was associated with increased likelihood, for being a recurrent faller in the subsequent 1.3 years following this time point.Conclusion: changes in gait parameters over several years are significantly associated with the likelihood of being a recurrent faller among community-dwelling older women at high risk of fracture. Further research is required to develop gait monitoring guidelines and gait parameter decline cut points that may be utilised by clinicians to identify older adults at risk of incident falls and sarcopenia.

Formalising industry feedback in assessment practice for work integrated learning

Work Integrated Learning (WIL) programs, such as co-operative education programs, offer students a learning environment which exists outside the traditional classroom and specific discipline boundaries. This necessitates assessment practices that go beyond traditional practices and relationships. Work placement supervisors are best located to observe the students' learning and development and offer feedback on performance and improvement. An investigation was carried out in order to articulate the role of supervisors and work colleagues in facilitating learning in the workplace environment and their involvement in assessment practices. Interviews were conducted with academics and industry representatives on their perceptions of assessment task validity in terms of professional and generic skill development. A synthesis of the findings formed the basis of the research described in this paper. Key findings highlighted the need for better preparation of industry supervisors as to their role in providing feedback and assessment; better preparation of students for transition from the classroom to the workplace; and the importance of ongoing involvement of the three stakeholders (academic, supervisor and student) to maximise learning and professional development. Recommendations for assessment practices include suggestions on how to involve employers in appraising workplace performance; how to measure the real life application of learning; and the development of generic employability skills. © Common Ground, Kathy Henschke, Joan Richardson.

Improving risk assessment in schizophrenia: epidemiological investigation of criminal history factors

Background : Violence risk assessment in schizophrenia relies heavily on criminal history factors.

Aims : To investigate which criminal history factors are most strongly associated with violent crime in schizophrenia.

Method : A total of 13 806 individuals (8891 men and 4915 women) with two or more hospital admissions for schizophrenia were followed up for violent convictions. Multivariate hazard ratios for 15 criminal history factors included in different risk assessment tools were calculated. The incremental predictive validity of these factors was estimated using tests of discrimination, calibration and reclassification.

Results : Over a mean follow-up of 12.0 years, 17.3% of men (n = 1535) and 5.7% of women (n = 281) were convicted of a violent offence. Criminal history factors most strongly associated with subsequent violence for both men and women were a previous conviction for a violent offence; for assault, illegal threats and/or intimidation; and imprisonment. However, only a previous conviction for a violent offence was associated with incremental predictive validity in both genders following adjustment for young age and comorbid substance use disorder.

Conclusions : Clinical and actuarial approaches to assess violence risk can be improved if included risk factors are tested using multiple measures of performance.

A new fuzzy peer assessment methodology for cooperative learning of students

In this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students.

Prosthetic motor imaginary task classification based on EEG quality assessment features

Brain Computer Interface (BCI) plays an important role in the communication between human and machines. This communication is based on the human brain signals. In these systems, users use their brain instead of the limbs or body movements to do tasks. The brain signals are analyzed and translated into commands to control any communication devices, robots or computers. In this paper, the aim was to enhance the performance of a brain computer interface (BCI) systems through better prosthetic motor imaginary tasks classification. The challenging part is to use only a single channel of electroencephalography (EEG). Arm movement imagination is the task of the user, where (s)he was asked to imagine moving his arm up or down. Our system detected the imagination based on the input brain signal. Some EEG quality features were extracted from the brain signal, and the Decision Tree was used to classify the participant's imagination based on the extracted features. Our system is online which means that it can give the decision as soon as the signal is given to the system (takes only 20 ms). Also, only one EEG channel is used for classification which reduces the complexity of the system which leads to fast performance. Hundred signals were used for testing, on average 97.4% of the up-down prosthetic motor imaginary tasks were detected correctly. This method can be used in many different applications such as: moving artificial limbs and wheelchairs due to it's high speed and accuracy.