{"id":4975,"date":"2023-09-14T10:38:37","date_gmt":"2023-09-14T00:38:37","guid":{"rendered":"https:\/\/www.optalert.com\/?p=4975"},"modified":"2026-09-21T15:04:46","modified_gmt":"2026-09-21T05:04:46","slug":"referencias-de-sonolencia-do-motorista-jds-kss-e-eeg","status":"publish","type":"post","link":"https:\/\/www.optalert.com\/pt\/driver-drowsiness-ground-truths-compared-jds-kss-and-eeg\/","title":{"rendered":"Sonol\u00eancia do motorista: Compara\u00e7\u00e3o das refer\u00eancias (ground truth) JDS, KSS e EEG"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
<\/div>\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

From mid-2024, all new models of passenger vehicles in Europe require a driver monitoring system (DMS) that can detect drowsiness. Engineers across the automotive industry have generally used one of three ground truths to measure drowsiness in drivers.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t
\n\t\t\t\n\t\t\t\t
Ground truth<\/div><\/div><\/div><\/th>
Description<\/div><\/div><\/div><\/th><\/tr><\/thead>\n\t\t\t\t\t\t\t\t
 JDS<\/strong> <\/div><\/div><\/div><\/td>
Johns Drowsiness Scale<\/div><\/div><\/div><\/td>
Eyelid movements<\/strong> are a window into the cognitive state of an individual. Optalert\u2019s Johns Drowsiness Scale (JDS) has been shown across dozens of studies to be the best predictor of a driver making an error due to drowsiness.<\/div><\/div><\/div><\/td><\/tr>
 KSS<\/strong> <\/div><\/div><\/div><\/td>
Karolinska Sleepiness Scale<\/div><\/div><\/div><\/td>
Subjective measures<\/strong> such as the Karolinska Sleepiness Scale (KSS) can be used as proxy metrics that correlate somewhat with the increase in relative risk.<\/div><\/div><\/div><\/td><\/tr>
 EEG<\/strong> <\/div><\/div><\/div><\/td>
Electro­encephalo­graphy<\/div><\/div><\/div><\/td>
Electrical activity in the brain<\/strong> is measured by electroencephalography (EEG), which becomes compromised in driving scenarios. EEG usually requires electrodes to be connected to the scalp, so all EEG-based drowsiness detection systems must use data from a non-invasive sensor to estimate EEG signals.<\/div><\/div><\/div><\/td><\/tr><\/tbody>\n\t\t\t<\/table>\n\t\t<\/div>\n\n\t\t<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

These three ground truths are vastly different in what they measure and how well they relate to the effect they are trying to measure in drivers: impairment<\/em>. For this purpose, impairment<\/em> can be defined as the increase in relative risk of a performance failure<\/em>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t
<\/div>\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

JDS: Eyelid movements and impairment<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Johns Drowsiness Scale (JDS)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

\"\"The Johns Drowsiness Scale (JDS) was developed specifically to protect drivers in real time. It was initially modelled around the laboratory-based Johns Test of Vigilance (JTV), which measures lapses in reactions to sudden visual stimuli. It was then refined by tracking performance failures in drivers in the field. Previously it had been understood that eyelid movements were linked to drowsiness. But nobody could have predicted just how accurately they would quantify the increase in relative risk during driving tasks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

\u201c<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

No technology other than Optalert\u2019s JDS provides a method for quantifying the \u2018relative risk of performance failure<\/strong>\u2019 in drivers, especially at its early stages.<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

– Dr. Murray Johns<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The JDS is derived from the driver\u2019s eyelid movements as captured via an RGB-IR camera in the DMS. The algorithm underpinning the JDS derives 64 mathematical parameters from the eyelid signal and tracks them over time. Its predictive power is valid across all ages, genders, and ethnicities, and has been deemed by Harvard Medical School to be commensurate with gold standard laboratory measures<\/a>. Further, across dozens of rigorously controlled independent studies it has been repeatedly shown to be the most accurate ground truth for predicting performance failures in driving tasks. One example was a study published in 2023 by a team at the Institute for Breathing and Sleep (IBAS)<\/a>:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t