An Automated Recording Method in Clinical Consultation to Rate the Limp in Lower Limb Osteoarthritis

For diagnosis and follow up, it is important to be able to quantify limp in an objective, and precise way adapted to daily clinical consultation. The purpose of this exploratory study was to determine if an inertial sensor-based method could provide simple features that correlate with the severity of lower limb osteoarthritis evaluated by the WOMAC index without the use of step detection in the signal processing. Forty-eight patients with lower limb osteoarthritis formed two severity groups separated by the median of the WOMAC index (G1, G2). Twelve asymptomatic age-matched control subjects formed the control group (G0). Subjects were asked to walk straight 10 meters forward and 10 meters back at self-selected walking speeds with inertial measurement units (IMU) (3-D accelerometers, 3-D gyroscopes and 3-D magnetometers) attached on the head, the lower back (L3-L4) and both feet. Sixty parameters corresponding to the mean and the root mean square (RMS) of the recorded signals on the various sensors (head, lower back and feet), in the various axes, in the various frames were computed. Parameters were defined as discriminating when they showed statistical differences between the three groups. In total, four parameters were found discriminating: mean and RMS of the norm of the acceleration in the horizontal plane for contralateral and ipsilateral foot in the doctor’s office frame. No discriminating parameter was found on the head or the lower back. No discriminating parameter was found in the sensor linked frames. This study showed that two IMUs placed on both feet and a step detection free signal processing method could be an objective and quantitative complement to the clinical examination of the physician in everyday practice. Our method provides new automatically computed parameters that could be used for the comprehension of lower limb osteoarthritis. It may not only be used in medical consultation to score patients but also to monitor the evolution of their clinical syndrome during and after rehabilitation. Finally, it paves the way for the quantification of gait in other fields such as neurology and for monitoring the gait at a patient’s home

MUMA: A Music Search Engine Based on Content Analysis

cote interne IRCAM: Lenoir11aNone / NoneNational audienceMUMA: A Music Search Engine Based on Content Analysi

The State of Black Criminology: A Focus Group of the Perspectives of African American Criminologists

According to Minnesota State University, Mankato, diversity is a commitment to create an understanding and appreciation of diverse peoples and diverse perspectives; a commitment to create an academic, cultural workplace environment and community that develops mutual respect for all and celebrates our differences. Realizing that all perspectives cannot be easily addressed by any one course, Diverse Culture Graduation Requirements implemented Corrections courses under its Goal1, that failed to address or include perspectives of African American criminologists. To date, the Criminal Justice textbooks used in Corrections 106 and Corrections 444 do not include any African American criminologist’s perspective. From this study we hoped to gain a better understanding of African American criminologist perspectives on criminal justice and corrections policy. We hoped that our findings would have an impact on the corrections department and its faculty as well as encourage the implementation of a more diverse curriculum. In order to gain a better insight into the perspective of African American criminologist a focus group was conducted to examine two issues; their attitudes towards criminal justice punitive policy in the criminal justice system and the exclusion of their perspectives within in the classroom

An Automated Recording Method in Clinical Consultation to Rate the Limp in Lower Limb Osteoarthritis

International audienceFor diagnosis and follow up, it is important to be able to quantify limp in an objective, and precise way adapted to daily clinical consultation. The purpose of this exploratory study was to determine if an inertial sensor-based method could provide simple features that correlate with the severity of lower limb osteoarthritis evaluated by the WOMAC index without the use of step detection in the signal processing. Forty-eight patients with lower limb osteoarthritis formed two severity groups separated by the median of the WOMAC index (G1, G2). Twelve asymptomatic age-matched control subjects formed the control group (G0). Subjects were asked to walk straight 10 meters forward and 10 meters back at self-selected walking speeds with inertial measurement units (IMU) (3-D accelerometers, 3-D gyroscopes and 3-D magnetometers) attached on the head, the lower back (L3-L4) and both feet. Sixty parameters corresponding to the mean and the root mean square (RMS) of the recorded signals on the various sensors (head, lower back and feet), in the various axes, in the various frames were computed. Parameters were defined as discriminating when they showed statistical differences between the three groups. In total, four parameters were found discriminating: mean and RMS of the norm of the acceleration in the horizontal plane for contralateral and ipsilateral foot in the doctor’s office frame. No discriminating parameter was found on the head or the lower back. No discriminating parameter was found in the sensor linked frames. This study showed that two IMUs placed on both feet and a step detection free signal processing method could be an objective and quantitative complement to the clinical examination of the physician in everyday practice. Our method provides new automatically computed parameters that could be used for the comprehension of lower limb osteoarthritis. It may not only be used in medical consultation to score patients but also to monitor the evolution of their clinical syndrome during and after rehabilitation. Finally, it paves the way for the quantification of gait in other fields such as neurology and for monitoring the gait at a patient’s home

Representative data and manual phase annotation result for one healthy participant performing a 10 meters go and 10 meters back walking exercise at self-selected walking speed.

<p>Black bars stand for manual annotation. Dashed zone corresponds to the walking phases. The walking parts of the signal were taken for parameter computation. (<b>A</b>)–Representative ML lateral angular velocity in the sensor linked frame for right foot. (<b>B</b>)—Representative ML lateral angular velocity in the sensor linked frame for left foot. (<b>C</b>)–Representative V angular velocity in the sensor linked frame for L3-L4.</p

Acceleration and angular velocity parameters in the sensor linked frames and the doctor’s office linked frame.

<p>RMS for root mean square.</p