Detection of Children Abuse by Voice and Audio Classification by Short-Time Fourier Transform Machine Learning implemented on Nvidia Edge GPU device

The safety of children in children home has become an increasing social concern, and the purpose of this experiment is to use machine learning applied to detect the scenarios of child abuse to increase the safety of children. This experiment uses machine learning to classify and recognize a child's voice and predict whether the current sound made by the child is crying, screaming or laughing. If a child is found to be crying or screaming, an alert is immediately sent to the relevant personnel so that they can perceive what the child may be experiencing in a surveillance blind spot and respond in a timely manner. Together with a hybrid use of video image classification, the accuracy of child abuse detection can be significantly increased. This greatly reduces the likelihood that a child will receive violent abuse in the nursery and allows personnel to stop an imminent or incipient child abuse incident in time. The datasets collected from this experiment is entirely from sounds recorded on site at the children home, including crying, laughing, screaming sound and background noises. These sound files are transformed into spectrograms using Short-Time Fourier Transform, and then these image data are imported into a CNN neural network for classification, and the final trained model can achieve an accuracy of about 92% for sound detection.Comment: 5 pages, 7 figures, PRAI 202

Bilateral globus pallidus interna deep brain stimulation in the treatment of mixed cerebral palsy in ataxia with dyskinesia: a case report

BackgroundCerebral palsy (CP), a complex syndrome with multiple etiologies, is characterized by a range of movement disorders within the hypokinetic and hyperkinetic spectrum (dystonia or choreoathetosis). CP is often accompanied by neurological and psychiatric signs, such as spasticity, ataxia, and cognitive disorders. Although current treatment options for CP include pharmacological interventions, rehabilitation programs, and spasticity relief surgery, their effectiveness remains limited. Deep brain stimulation (DBS) has demonstrated significant effectiveness in managing dyskinesia; however, its potential therapeutic effect on CP remains determined.MethodsWe present a case of a 44-year-old Asian female who was born as a twin with neonatal ischemic–hypoxic encephalopathy due to prolonged labor and delivery. She was diagnosed with CP at the age of 1 year. The patient exhibited delayed development compared to her peers and presented with various symptoms, including slurred speech, broad-based gait, horseshoe inversion of the right lower extremity, involuntary shaking of the upper extremities bilaterally, and hypotonia and showed no improvement with levodopa therapy. Two years ago, she developed progressive head tremors, which worsened during periods of tension and improved during sleep. As medical treatments proved ineffective and there were no contraindications to surgery, we performed bilateral globus pallidus interna DBS (GPi-DBS) to alleviate her motor dysfunction.ResultsFollowing a 6-month follow-up, the patient demonstrated significant improvements in motor symptoms, including head and limb tremors and dystonia. In addition, significant improvement was observed in her overall psychological well-being, as evidenced by reduced anxiety and depression levels.ConclusionDBS is an effective treatment for dyskinesia symptoms associated with CP in adults. Moreover, its effectiveness may continue to increase over time