{
  "id": 393574,
  "title": "Freezing of gait (FoG) ANOVA, 88.09% accuracy, 77.58% precision.",
  "url": "/competitions/tlvmc-parkinsons-freezing-gait-prediction/discussion/393574",
  "author_name": "",
  "post_date": "2023-03-09T21:36:22.818239900Z",
  "votes": 29,
  "comment_count": 1,
  "views": 0,
  "content": "<h1>Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors</h1>\n<p>Citation: Ren K, Chen Z, Ling Y, Zhao J. Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors. BMC Neurol. 2022 Jun 21;22(1):229. doi: 10.1186/s12883-022-02732-z. PMID: 35729546; PMCID: PMC9210754.</p>\n<p>\" This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients.\"</p>\n<p>\"Firstly, the authors used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, they evaluated the detection effects with different combinations of sensors to get the best sensors configuration.</p>\n<p>\"Finally, the authors selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved.\" </p>\n<p>\"This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.\"</p>\n<p><a href=\"https://pubmed.ncbi.nlm.nih.gov/35729546/\" target=\"_blank\">https://pubmed.ncbi.nlm.nih.gov/35729546/</a></p>\n<h1>Freezing of gait in Parkinson's disease: where are we now?</h1>\n<p>Citation: Heremans E, Nieuwboer A, Vercruysse S. Freezing of gait in Parkinson's disease: where are we now? Curr Neurol Neurosci Rep. 2013 Jun;13(6):350. doi: 10.1007/s11910-013-0350-7. PMID: 23625316.</p>\n<p>\"Freezing of gait (FOG) is defined as a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. It is one of the most debilitating motor symptoms in patients with Parkinson's disease (PD) as it may lead to falls and a loss of independence.\"</p>\n<p><a href=\"https://pubmed.ncbi.nlm.nih.gov/23625316/\" target=\"_blank\">https://pubmed.ncbi.nlm.nih.gov/23625316/</a></p>\n<h1>Information, one great way to manage and treat Parkinson.</h1>",
  "messages": [
    {
      "id": "2175478",
      "postDate": "03/09/2023 21:36:22",
      "content": "<h1>Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors</h1>\n<p>Citation: Ren K, Chen Z, Ling Y, Zhao J. Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors. BMC Neurol. 2022 Jun 21;22(1):229. doi: 10.1186/s12883-022-02732-z. PMID: 35729546; PMCID: PMC9210754.</p>\n<p>\" This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients.\"</p>\n<p>\"Firstly, the authors used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, they evaluated the detection effects with different combinations of sensors to get the best sensors configuration.</p>\n<p>\"Finally, the authors selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved.\" </p>\n<p>\"This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.\"</p>\n<p><a href=\"https://pubmed.ncbi.nlm.nih.gov/35729546/\" target=\"_blank\">https://pubmed.ncbi.nlm.nih.gov/35729546/</a></p>\n<h1>Freezing of gait in Parkinson's disease: where are we now?</h1>\n<p>Citation: Heremans E, Nieuwboer A, Vercruysse S. Freezing of gait in Parkinson's disease: where are we now? Curr Neurol Neurosci Rep. 2013 Jun;13(6):350. doi: 10.1007/s11910-013-0350-7. PMID: 23625316.</p>\n<p>\"Freezing of gait (FOG) is defined as a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. It is one of the most debilitating motor symptoms in patients with Parkinson's disease (PD) as it may lead to falls and a loss of independence.\"</p>\n<p><a href=\"https://pubmed.ncbi.nlm.nih.gov/23625316/\" target=\"_blank\">https://pubmed.ncbi.nlm.nih.gov/23625316/</a></p>\n<h1>Information, one great way to manage and treat Parkinson.</h1>",
      "rawMarkdown": "#Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors\n\nCitation: Ren K, Chen Z, Ling Y, Zhao J. Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors. BMC Neurol. 2022 Jun 21;22(1):229. doi: 10.1186/s12883-022-02732-z. PMID: 35729546; PMCID: PMC9210754.\n\n\" This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients.\"\n\n\"Firstly, the authors used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, they evaluated the detection effects with different combinations of sensors to get the best sensors configuration.\n\n\"Finally, the authors selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved.\" \n\n\"This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.\"\n\nhttps://pubmed.ncbi.nlm.nih.gov/35729546/\n\n#Freezing of gait in Parkinson's disease: where are we now?\n\nCitation: Heremans E, Nieuwboer A, Vercruysse S. Freezing of gait in Parkinson's disease: where are we now? Curr Neurol Neurosci Rep. 2013 Jun;13(6):350. doi: 10.1007/s11910-013-0350-7. PMID: 23625316.\n\n\"Freezing of gait (FOG) is defined as a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. It is one of the most debilitating motor symptoms in patients with Parkinson's disease (PD) as it may lead to falls and a loss of independence.\"\n\nhttps://pubmed.ncbi.nlm.nih.gov/23625316/\n\n#Information, one great way to manage and treat Parkinson.",
      "votes": null
    },
    {
      "id": "2337628",
      "postDate": "07/10/2023 08:50:52",
      "content": "<p>suggest few Ai /ml/dl approaches to use and \\\\</p>",
      "rawMarkdown": "suggest few Ai /ml/dl approaches to use and \\\\\\\\",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2337628,
      "author_name": "manav2021",
      "author_url": "",
      "post_date": "07/10/2023 08:50:52",
      "content": "<p>suggest few Ai /ml/dl approaches to use and \\\\</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2175478": "#Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors\n\nCitation: Ren K, Chen Z, Ling Y, Zhao J. Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors. BMC Neurol. 2022 Jun 21;22(1):229. doi: 10.1186/s12883-022-02732-z. PMID: 35729546; PMCID: PMC9210754.\n\n\" This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients.\"\n\n\"Firstly, the authors used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, they evaluated the detection effects with different combinations of sensors to get the best sensors configuration.\n\n\"Finally, the authors selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved.\" \n\n\"This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.\"\n\nhttps://pubmed.ncbi.nlm.nih.gov/35729546/\n\n#Freezing of gait in Parkinson's disease: where are we now?\n\nCitation: Heremans E, Nieuwboer A, Vercruysse S. Freezing of gait in Parkinson's disease: where are we now? Curr Neurol Neurosci Rep. 2013 Jun;13(6):350. doi: 10.1007/s11910-013-0350-7. PMID: 23625316.\n\n\"Freezing of gait (FOG) is defined as a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. It is one of the most debilitating motor symptoms in patients with Parkinson's disease (PD) as it may lead to falls and a loss of independence.\"\n\nhttps://pubmed.ncbi.nlm.nih.gov/23625316/\n\n#Information, one great way to manage and treat Parkinson.",
    "2337628": "suggest few Ai /ml/dl approaches to use and \\\\\\\\"
  },
  "source": "meta"
}