Yazarlar, teknik ve veri toplama yardım için Evan Beisheim, Nicole Capela, Andrew Herbert-Copley kabul etmiş sayılırsınız. Proje finansman Çalışmada kullanılan akıllı telefonlar da dahil olmak üzere Doğa Bilimleri ve Kanada&#39;nın (NSERC) Mühendislik Araştırma Konseyi ve BlackBerry Ltd., alındı.

Bu protokol Ottawa Sağlık Bilimleri Ağı Araştırma Etik Kurulu tarafından onaylanmıştır. 1. Hazırlık <ol><li> , Bir araştırma taslak katılımcılara sağlayın sorulara cevap ve aydınlatılmış onam. Kayıt katılımcı özellikleri (örneğin, yaş, cinsiyet, boy, kilo, bel çevresi, anterior superior medial malleol için iliyak bacak yükseklik) Bir veri sayfasında, kimlik kodu, tarih ve. Video yakalamak için kullanılan ikinci akıllı telefon, ikinci yakalama hızı başına en az 30 kare olarak ayarlanmış olduğundan emin olun. </li><li> Güvenli katılımcının sağ ön kemer veya pantolon bel için bir telefon kılıfına ekleyin. Hareketlilik ölçüm smartphone sensör verileri (yani veri kaydı veya WMMS uygulaması) toplamak ve uygulama uygun çalıştığından emin olmak için kullanılacak akıllı telefon uygulamasını başlatın. Cihazın arkasındaki (arka ca ile tutucuya yerleştirin smartphonedışa bakan mera). </li><li> İkinci smartphone dijital video kayıt başlayın. Anonimlik için kişinin yüzünü göstermeden karşılaştırma video kaydetmek, ancak tüm faaliyet geçişleri kaydetmek için emin olun. Telefon el olabilir. </li></ol> 2. Etkinlik Devre <ol><li> İkinci smartphone, onların eylemleri katılımcıyı ve videoyu izleyin onlar araştırmacı tarafından konuşulan şu eylemleri gerçekleştirmek ise: <ol><li> Ayakta pozisyondan, duruşmanın başladığını göstermek için smartphone sallayın. </li><li> En az 10 sn bekletildikten devam edin. Bu duran faz, telefon yönlendirmesi kalibrasyon 14 için kullanılabilir. </li><li> Yakındaki bir sandalyeye yürüyün ve otur. </li><li> Ayağa kalk ve bir asansöre 60 metre yürüyün. </li><li> Standı ve asansör için bekleyin ve daha sonra asansöre yürümek. </li><li> İkinci katta asansörün atın. </li><li> Çevirin ve ev ortamına yürüyün. </li><li> Banyo ve si içine yürümekMulate diş fırçalama. </li><li> Saç tarama benzetin. </li><li> El yıkama benzetin. </li><li> Bir havlu kullanarak Kuru eller. </li><li> Mutfağa yürüyün. </li><li> Bir raf yemekleri alın ve tezgahın üzerine koyun. </li><li> Evye su ile bir su ısıtıcısı doldurun. </li><li> Soba elemanı üzerindeki su ısıtıcısı yerleştirin. </li><li> Bir tost ekmeği yerleştirin. </li><li> Yemek odası yürüyün. </li><li> Yemek odası masada oturmak. </li><li> Masada yemek yeme benzetin. </li><li> Standı ve geri evye yürüyün. </li><li> Yemekleri durulayın ve bir rafa koyun. </li><li> Asansör geri mutfaktan yürüyün. </li><li> Standı ve asansör için bekleyin ve daha sonra asansöre yürümek. </li><li> Birinci katta asansörün atın. </li><li> Bir merdiven boşluğu 50 metre yürüyün. </li><li> Kapıyı açın ve merdiven boşluğu girin. </li><li> (Iniş yaklaşık 13 adımları, 13 adım) merdivenlerden yukarı yürüyün. </li><li> Koridorda merdiven kapısını açın. </li><li> Sağa dönün ve 15 metre koridordan yürüyün. </li><li> Arkanı dön ve geri merdiven boşluğu 15 metre yürüyün. </li><li> Kapıyı açın ve merdiven boşluğu girin. </li><li> (Iniş yaklaşık 13 adımları, 13 adım) merdivenlerden aşağı yürüyün. </li><li> Merdiven boşluğu çıkın ve bir odaya yürüyün. </li><li> Bir yatakta yatmak. </li><li> Kalk ve bir rampa 10 metre yürümek. </li><li> Daha sonra rampa (20 metre) aşağı dönüp, rampa yukarı yürüyün. </li><li> Salona yürümeye devam ve dışarıdan kapıyı açın. </li><li> Döşeli yolu üzerinde 100 metre yürüyün. </li><li> Arkanı dön ve odasına geri yürüyün. </li><li> Odasına yürümek ve başlangıç ​​noktasında duruyor. </li><li> Ayakta Devam ve ardından davanın sonunu belirtmek için akıllı sallayın. </li></ol></li></ol> 3. Deneme Tamamlama <ol><li> Video kayıt akıllı telefon durdurun ve kaldırmak ve akıllı ve kılıfından dönmek için katılımcıdan. Sma veri günlüğü ya da WMMS uygulamayı durdurmakrtphone. Post-processing için bir bilgisayara hem telefonlarından elde edilen hareket veri dosyalarını ve video dosyasını kopyalayın. </li></ol> 4. Post-processing <ol><li> Shake eylemi başladığında vakit belirleyerek video ve ham sensör verileri arasındaki zamanlamayı senkronize edin. Bu sallayarak hareket ayrı bir ivme sinyali ve video karesine eşittir. Sensör ve video veri kaynakları için, başlangıç ​​sallamak zaman bitiş süresini shake çıkarılarak eşitleme hatası olup olmadığını kontrol edin. Zaman farkı iki veri kümesi arasındaki benzer olmalıdır. </li><li> Faaliyetleri arasındaki geçişte video karesine başlangıç ​​sallamak zaman zaman farkı kaydederek altın standart video gerçek değişim-of-devlet sürelerini belirler. 0.033 sn dahilinde zamanlama elde etmek için video düzenleme yazılımı kullanın (örneğin, ikinci bir video oranı 30 kare). Karşılaştırılabilir değişiklikler eyalet sensör verileri oluşturmak için WMMS yazılımını kullanın. </li><li> Iki veri setleri oluşturmak, tekGerçek faaliyetleri ve tahmin faaliyetleri ile ikinci olan her bir video çerçevesi için aktiviteyi etiketleyerek WMMS çıkışı her video çerçeve anda tahmin faaliyeti hesaplanması ve sonra (devlet zamanlaması değişikliğe göre). WMMS performans değerlendirmesi için altın standart aktivite arasındaki gerçek pozitifler, yanlış negatif, gerçek negatifler, yanlış pozitif hesaplamak ve WMMS aktivitesini öngördü. Duyarlılık, seçicilik ve F-skor sonuçları önlemler hesaplamak için bu parametreleri kullanın. Not: analiz edilen pencerenin her iki tarafında 3 veri pencerelerin bir tolerans ayar değişikliği eyalet sonuçlarını belirlemek için kullanılan ve sınıflandırma sonuçları için 2 veri pencereleri edilebilir. 1 ikinci veri pencereler bu çalışmada WMMS kullanıldı çünkü bu tolerans dahilinde ardışık değişiklikler göz ardı edilir, böylece Örneğin, 3 sn önce ve geçerli pencerenin sonra muayene edildi. Dikkate 3 saniyeden daha kısa meydana devlet değişiklikler brüt huma için göz ardı edilebilir olduğunuBu devletlerin beri n hareket analizi geçici olarak düşünülebilir. </li></ol>

<ol>
	<li>Lara, O. D., Labrador, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Survey+on+Human+Activity+Recognition+using+Wearable+Sensors.">A Survey on Human Activity Recognition using Wearable Sensors.</a> IEEE Communications Surveys Tutorials. 15 (3), 1192-1209 (2013).</li><li>Foerster, F., Smeja, M., Fahrenberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+posture+and+motion+by+accelerometry:+a+validation+study+in+ambulatory+monitoring.">Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring.</a> Computers in Human Behavior. 15 (5), 571-583 (1999).</li><li>Elsmore, T. F., Naitoh, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Monitoring Activity With a Wrist-Worn Actigraph: Effects of Amplifier Passband and threshold Variations. , (1993).</li><li>Kripke, D. F., Webster, J. B., Mullaney, D. J., Messin, S., Mason, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Measuring sleep by wrist actigraph. , (1981).</li><li>Lockhart, J. W., Pulickal, T., Weiss, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+mobile+activity+recognition.">Applications of mobile activity recognition.</a> Proceedings of the 2012 ACM Conference on Ubiquitous Computing. , 1054-1058 (2012).</li><li>Incel, O. D., Kose, M., Ersoy, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Review+and+Taxonomy+of+Activity+Recognition+on+Mobile+Phones.">A Review and Taxonomy of Activity Recognition on Mobile Phones.</a> BioNanoScience. 3 (2), 145-171 (2013).</li><li>Yang, C. C., Hsu, Y. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Review+of+Accelerometry-Based+Wearable+Motion+Detectors+for+Physical+Activity+Monitoring.">A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring.</a> Sensors. 10 (8), 7772-7788 (2010).</li><li>He, Y., Li, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physical+Activity+Recognition+Utilizing+the+Built-In+Kinematic+Sensors+of+a+Smartphone.">Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone.</a> International Journal of Distributed Sensor Networks. 2013, (2013).</li><li>Vo, Q. V., Hoang, M. T., Choi, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Personalization+in+Mobile+Activity+Recognition+System+using+K-medoids+Clustering+Algorithm.">Personalization in Mobile Activity Recognition System using K-medoids Clustering Algorithm.</a> International Journal of Distributed Sensor Networks. 2013, (2013).</li><li>Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L., Cook, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simple+and+Complex+Activity+Recognition+through+Smart+Phones.">Simple and Complex Activity Recognition through Smart Phones.</a> , 214-221 (2012).</li><li>Hache, G., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mobility+change-of-state+detection+using+a+smartphone-based+approach.">Mobility change-of-state detection using a smartphone-based approach.</a> IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA). , 43-46 (2010).</li><li>Wu, H. H., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Change-of-state+determination+to+recognize+mobility+activities+using+a+BlackBerry+smartphone.">Change-of-state determination to recognize mobility activities using a BlackBerry smartphone.</a> , 5252-5255 (2011).</li><li>Wu, H., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Activity+Change-of-state+Identification+Using+a+Blackberry+Smartphone.">Activity Change-of-state Identification Using a Blackberry Smartphone.</a> Journal of Medical and Biological Engineering. 32 (4), 265-271 (2012).</li><li>Tundo, M. D., Lemaire, E., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correcting+Smartphone+orientation+for+accelerometer-based+analysis.">Correcting Smartphone orientation for accelerometer-based analysis.</a> IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA). , 58-62 (2013).</li><li>Tundo, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Development of a human activity recognition system using inertial measurement unit sensors on a smartphone. , (2014).</li></ol>

The authors declare that they have no competing financial interests.

Bir giyilebilir hareketlilik izleme sistemi ile insan faaliyetinin tanıma nedeniyle giyilebilir bilgisayar ve akıllı telefonlar ve klinik karar verme ve sağlık müdahalesi değerlendirmemize yardımcı kantitatif sonuç ölçümlerinin sistematik ihtiyaçlarını teknik gelişmeler son yıllarda daha fazla ilgi görmüştür. Bu yazıda anlatılan metodoloji etkinliği sınıflandırması hataları günlük yaşam aktiviteleri ve yürüyen senaryolar geniş bir yelpazede değerlendirmeye dahil olmasaydı mevcut olm...

Ubiquitous algılama nedeniyle giderek güçlü, küçük, düşük maliyetli bilgisayar ve donanımları 1 algılama için ilgi çekici bir araştırma alanı haline gelmiştir. Tüketici düzeyinde mikroelektronik yüksek doğruluk 1 ile hareket özelliklerini saptayabilen beri giyilebilir sensörler kullanılarak Hareketlilik izleme ilgi bir hayli üretti. 4 - ön çalışmalar 2 1990&#39;lar 1980&#39;lerde gerçekleştirilen ve birlikte giyilebilir sensörler kullanılarak insan faaliyeti tanıma (HAR), araştırma yeni bir alandır. Modern akıllı telefonlar hareketlilik faaliyeti tanınması için gerekli sensörler ve gerçek zamanlı hesaplama yeteneği içerir. Cihaz üzerindeki gerçek zamanlı analiz kullanıcı veya araştırmacı müdahalesi olmadan faaliyet sınıflandırma ve veri yükleme izin verir. Hareket analiz yazılımı ile bir akıllı telefon tespiti, ev veya iş otomasyonu ve kendi kendini yöneten exercis sonbahar, spor izleme, sağlık izleme sağlayabilire programları 5. Akıllı telefonlar onboard sensör çıkışları 6 ile hesaplanan oluşturulan matematiksel sinyal özelliklerini kullanarak, insanlarda mobil faaliyetleri ve mobil desenleri tespit etmek için atalet ölçüm platformları olarak kabul edilebilir. Ortak özellik nesil yöntemleri sezgisel, zaman etki, frekans domain ve dalgacık analizi temelli yaklaşımlar 7 arasındadır. Belirtilen faaliyetleri 1,5,6,7 tespit ettiğinde, modern akıllı telefon HAR sistemleri, yüksek tahmin doğrulukları göstermiştir. Çalışmaların çoğu, kendi eğitim seti, çevre kurulum ve veri toplama protokolü beri bu çalışmalar değerlendirme metodolojisi yanı sıra doğruluk değişebilir. Duyarlılık, özgüllük, doğruluk, hatırlama, hassas ve F-Puan yaygın tahmin kalitesini tanımlamak için kullanılır. Bununla birlikte, herhangi bir bilgi, küçük, gerçek zamanlı olarak aktivite değişiklikleri algılamak için yeteneği &quot;eşzamanlı aktivitesi&quot; tanıma ve değerlendirilmesi için yöntemler mevcuttur1, çeşitli etkinlikler kategorize girişiminde HAR sistemleri. HAR sistem hassasiyeti için Değerlendirme yöntemleri çalışmalar arasında önemli ölçüde değişir. Ne olursa olsun sınıflandırma algoritması veya uygulamalı özelliklerin, altın standart değerlendirme yöntemlerinin açıklamaları çoğu HAR araştırma için belirsizdir. Bir günlük yaşam ortamında Etkinlik tanıma yoğun araştırılmış değil. En akıllı telefon-temelli etkinlik tanıma sistemi, gerçek dünya ortamına algoritması yerine gerçekçi avantajlı olabilir bir değerlendirme protokolü yol açan, kontrollü bir şekilde değerlendirilir. Bunların değerlendirilmesi düzeni içinde, katılımcılar genellikle oldukça gerçek yaşam olaylarını taklit, ard arda gerçekleştirmek için katılımcı için gerçekçi faaliyetleri geniş bir ürün yelpazesi uygulamak daha tahmin yönelik sadece eylemleri gerçekleştirmek. Bazı akıllı telefon HAR tür merdiven ve yürüyüş gibi birlikte 8,9 grup benzer faaliyetleri, çalışmalarıAncak veri kümesinden diğer faaliyetleri hariç. Tahmin doğruluğu daha sonra algoritma hedef faaliyetlerini tespit ne kadar iyi belirlenir. Dernbach ve ark., 9 katılımcıların sürekli değişim-of-devlet geçişler kesintiye, hareket etmeden önce yürütmek için hakkında vardı yazma etkinliği vardı. Katılımcı bir günlük yaşam ortamında doğal eylemleri gerçekleştirir iken HAR sistem değerlendirmeler algoritması değerlendirmelidir. Bu uygulamanın günlük kullanım çoğaltır gerçek hayatta değerlendirilmesine olanak olacaktır. Gerçekçi bir devre birçok devlet değişikliklerini-yanı sıra sistem tarafından predicable olmayan eylemlerin bir karışımını içerir. Bir araştırmacı sonra bu şekilde anormal hareketlere algoritmanın sağlamlığını değerlendirmek, bu ek hareketlere algoritmanın yanıtını değerlendirmek olabilir. Bu kağıt gerçek yaşam günlük yaşam ortamlarını yansıtan kontrollü bir kurs kullanan Giyilebilir Mobilite İzleme Sistemi (WMMS) değerlendirme protokolü sunar. WMMSDeğerlendirme sonra kontrollü ama gerçekçi koşullar altında yapılabilir. Bu protokol, biz Ottawa Üniversitesi ve Ottawa Hospital Research Institute 11-15 geliştirildi üçüncü nesil WMMS kullanın. WMMS bir tri-eksenli ivmeölçer ve jiroskop ile akıllı telefonlar için tasarlanmıştır. Hareketlilik algoritması, kullanıcı değişkenliği hesapları değişiklik eyalet belirlenmesi için yanlış pozitif sayısında bir azalma sağlar ve etkinlik kategorizasyon hassasiyetini artırır. WMMS daha WMMS sınıflandırma geliştirir bağlam-duyarlı etkinlik değerlendirilmesi için devlet faaliyeti değişiklikleri tespit edilir kısa video klip kaydı tetikler çünkü yanlış pozitif en aza indirilmesi önemlidir. Gereksiz bir video kayıt depolama ve pil kullanımı yetersizliklere oluşturur. WMMS algoritması tahmin düzeyinde bir artış miktarında bir artış anlamına farklı tahmin seviyelerini kullanarak düşük hesaplama öğrenme modeli olarak yapılandırılmıştır ve değerlendirilirtanınabilir eylemler.

<table border="0" cellpadding="0" cellspacing="0" width="1314">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:516px;">Smartphone or wearable measurement device</td>
			<td style="width:627px;">Blackberry</td>
			<td style="width:108px;">Z10</td>
			<td style="width:64px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Smartphone for video recording</td>
			<td>Blackberry</td>
			<td>Z10 or 9800</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phone holster</td>
			<td>Any</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Data logger application for the smartphone</td>
			<td>BlackBerry World - TOHRC Data Logger for BlackBerry 10</td>
			<td></td>
			<td><a href="http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA">http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA</a></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Wearable mobility measurement</td>
			<td>Custom Blackberry 10 and Matlab software for mobility monitoring</td>
			<td></td>
			<td><a href="http://www.irrd.ca/cag/smartphone/">http://www.irrd.ca/cag/smartphone/</a></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Video editing or analysis software</td>
			<td>Motion Analysis Tools</td>
			<td></td>
			<td><a href="http://www.irrd.ca/cag/mat/">http://www.irrd.ca/cag/mat/</a></td>
		</tr>
	</tbody>
</table>

evaluation of a smartphone-based human activity recognition system in a daily living environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

Çalışma protokolü yüksekliği cm (11,4 ±) 173,9 idi, kimin ortalama ağırlığı 68.9 idi (11.1 ±) onbeş-güçlü gövdeli katılımcıların bir kolaylık numunesi kg yapılan ve yaş Ottawa Hastanesi işe, 26 (± 9) yıl idi ve Ottawa personelin ve öğrencilerin Üniversitesi. Bir akıllı telefon değişken 40-50 Hz hızında sensör verileri ele geçirdi. Örnek oranı varyasyonları smartphone sensörü örnekleme için tipiktir. İkinci akıllı telefon 1280x720 (720p) çözünürlükte dijital video kay...

Watch this Scientific Journal Video about Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment at JoVE.com

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

Bir Günlük Yaşam Ortamında Smartphone merkezli İnsan Aktivitesi Tanıma Sisteminin Değerlendirilmesi

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

evaluation-smartphone-based-human-activity-recognition-system-daily

Research

JoVE Journal

Behavior

8.8K Görüntüleme.  University of Ottawa. A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

Video: Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Construction of Microdrive Arrays for Chronic Neural Recordings in Awake Behaving Mice

State-of-the-art electrophysiological recordings from the brains of freely behaving animals allow researchers to simultaneously examine local field potentials (LFPs) from populations of neurons and action potentials from individual cells, as the animal engages in experimentally relevant tasks. Chronically implanted microdrives allow for brain recordings to last over periods of several weeks. Miniaturized drives and lightweight components allow for these long-term recordings to occur in small mammals, such as mice. By using tetrodes, which consist of tightly braided bundles of four electrodes in which each wire has a diameter of 12.5 &mu;m, it is possible to isolate physiologically active neurons in superficial brain regions such as the cerebral cortex, dorsal hippocampus, and subiculum, as well as deeper regions such as the striatum and the amygdala. Moreover, this technique insures stable, high-fidelity neural recordings as the animal is challenged with a variety of behavioral tasks. This manuscript describes several techniques that have been optimized to record from the mouse brain. First, we show how to fabricate tetrodes, load them into driveable tubes, and gold-plate their tips in order to reduce their impedance from M&Omega; to K&Omega; range. Second, we show how to construct a custom microdrive assembly for carrying and moving the tetrodes vertically, with the use of inexpensive materials. Third, we show the steps for assembling a commercially available microdrive (Neuralynx VersaDrive) that is designed to carry independently movable tetrodes. Finally, we present representative results of local field potentials and single-unit signals obtained in the dorsal subiculum of mice. These techniques can be easily modified to accommodate different types of electrode arrays and recording schemes in the mouse brain.

The design and assembly of microdrives for in vivo electrophysiological recordings of brain signals from the mouse is described. By attaching microelectrode bundles to sturdy driveable carriers, these techniques allow for long-term and stable neural recordings. The lightweight design allows for unrestricted behavioral performance by the animal following drive implantation.

Uyanık davranmak Fare Kronik Sinir Kayıtlar için Microdrive Diziler İnşaatı

State-of-the-art electrophysiological recordings from the brains of freely behaving animals allow researchers to simultaneously examine local field ...

Davranış

Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically identified cells during behavior1-6. Here we describe methods to perform two-photon imaging in mouse cortex while the animal navigates a virtual reality environment. We focus on the aspects of the experimental procedures that are key to imaging in a behaving animal in a brightly lit virtual environment. The key problems that arise in this experimental setup that we here address are: minimizing brain motion related artifacts, minimizing light leak from the virtual reality projection system, and minimizing laser induced tissue damage. We also provide sample software to control the virtual reality environment and to do pupil tracking. With these procedures and resources it should be possible to convert a conventional two-photon microscope for use in behaving mice.

Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.

Sanal Gerçeklik Çevre gezinme Farelerde iki foton Kalsiyum Görüntüleme

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically ...

Development of a Virtual Reality Assessment of Everyday Living Skills

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; Treatment studies aimed at cognitive impairment in patients with schizophrenia not only require demonstration of improvements on cognitive tests, but also evidence that any cognitive changes lead to clinically meaningful improvements.&#160; Measures of &#8220;functional capacity&#8221; index the extent to which individuals have the potential to perform skills required for real world functioning. &#160;Current data do not support the recommendation of any single instrument for measurement of functional capacity.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a novel, interactive gaming based measure of functional capacity that uses a realistic simulated environment to recreate routine activities of daily living. Studies are currently underway to evaluate and establish the VRFCAT&#8217;s sensitivity, reliability, validity, and practicality.&#160;This new measure of functional capacity is practical, relevant, easy to use, and has several features that improve validity and sensitivity of measurement of function in clinical trials of patients with CNS disorders.

A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

Gündelik Yaşam Becerileri bir Sanal Gerçeklik Değerlendirmesi Gelişimi

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

A Protocol for Housing Mice in an Enriched Environment

Environmental enrichment (EE) is a housing environment for mice that boosts mental and physical health compared to standard laboratory housing. Our recent studies demonstrate that environmental enrichment decreases adiposity, increases energy expenditure, resists diet induced obesity, and causes cancer remission and inhibition in mice. EE typically consists of larger living space, a variety of &#8216;toys&#8217; to interact with, running wheels, and can include a number of other novel environmental changes. All of this fosters a more complex social engagement, cognitive and physical stimulations. Importantly, the toy location and type of toy is changed regularly, which encourages the mice to adapt to a frequently changing and novel environment. Many variables can be manipulated in EE to promote health effects in mice. Thus these approaches are difficult to control and must be properly managed to successfully replicate the associated phenotypes. Therefore, the goal of this video is to demonstrate how EE is properly set up and maintained to assure a complex, challenging, and controlled environment so that other researchers can easily reproduce the protective effects of EE against obesity and cancer.

Environmental enrichment for mice requires a complex and challenging setup, as well as comprehensive husbandry and handling techniques to assure robust metabolic and anti-cancer effects in the mice. This protocol provides detailed procedures to reproduce the above mentioned effects in mice.

Bir Zenginleştirilmiş Ortamında Konut Fareler için Protokol

Environmental enrichment (EE) is a housing environment for mice that boosts mental and physical health compared to standard laboratory housing. Our recent ...

A Method to Study Adaptation to Left-Right Reversed Audition

An unusual sensory space is one of the effective tools to uncover the mechanism of adaptability of humans to a novel environment. Although most of the previous studies have used special spectacles with prisms to achieve unusual spaces in the visual domain, a methodology for studying the adaptation to unusual auditory spaces has yet to be fully established. This study proposes a new protocol to set-up, validate, and use a left-right reversed stereophonic system using only wearable devices, and to study the adaptation to left-right reversed audition with the help of neuroimaging. Although individual acoustic characteristics are not yet implemented, and slight spillover of unreversed sounds is relatively uncontrollable, the constructed apparatus shows high performance in a 360&#176; sound source localization coupled with hearing characteristics with little delay. Moreover, it looks like a mobile music player and enables a participant to focus on daily life without arousing curiosity or drawing attention of other individuals. Since the effects of adaptation were successfully detected at the perceptual, behavioral, and neural levels, it is concluded that this protocol provides a promising methodology for studying adaptation to left-right reversed audition, and is an effective tool for uncovering the adaptability of humans to a novel environments in the auditory domain.

The present study proposes a protocol to investigate the adaptation to left-right reversed audition achieved only by wearable devices, using neuroimaging, which can be an effective tool for uncovering the adaptability of humans to a novel environment in the auditory domain.

-Sol uyum eğitim için bir yöntem seçme ters

An unusual sensory space is one of the effective tools to uncover the mechanism of adaptability of humans to a novel environment. Although most of the ...

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

Reciprocal social behavior (RSB), an early-emerging capacity to engage in social contingency&#8212;which is foundational for both social learning and social competency&#8212;is hypothesized to be disrupted in autism spectrum disorder (ASD). The ability to quantify the full range of RSB during the toddler period, when core symptoms of ASD often arise, is pivotal for evaluating early risk for ASD, characterizing social development, and tracking response to early interventions. However, important parameters of variation in RSB&#8212;especially prior to the development of verbal language&#8212;can be nuanced and difficult to characterize using questionnaire-based methods. To address this challenge, we developed a system for measuring quantitative variation in RSB in toddlers (ages 18 - 30 months) that incorporated not only standard questionnaire data from caregivers but also a novel set of video-referenced items, through which a respondent compares the behavior of a subject to that observed in a short video of a young child manifesting a highly competent level of social communication. Testing of this measure in a general population sample of twins confirmed that both the video-referenced items and the RSB Total Score (video-referenced items plus non-video-referenced items) displayed unimodal, continuous distributions, strong internal consistency, marked preservation of individual differences, and extremely high heritability. In addition, video-referenced items were particularly sensitive to quantifying incremental changes in social communication, a major element of RSB, over the course of early childhood development. Scores on the vrRSB clearly differentiated children with and without ASD and these data comprise an initial validation of this promising method for quantifying early RSB&#8212;cross-sectionally, over time, and as a function of early intervention.

Here we present a protocol for administration of the video-referenced rating of reciprocal social behavior (vrRSB) using a paper or online version. This survey quantifies RSB, a prerequisite for social competence, in toddlers through &#34;video-referenced&#34; items whereby a subject is compared to a reference child shown in a three-minute video.

Toddlers yılında sosyal iletişim hızlı seri değerlendirmesi için çapa puanlama bir Video kullanımı

Reciprocal social behavior (RSB), an early-emerging capacity to engage in social contingency&#8212;which is foundational for both social learning and ...

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

Sanal gerçeklik içinde günlük yaşam hareketleri hareket yakalama sistemleri ile kinematik ölçme

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various physiological or pathological changes. The object location task (OLT) and the novel object recognition task (NORT) are two effective behavioral tasks commonly used to reveal the function and relative health of specific brain regions involved in memory. While both of these tests exploit the inherent preference of mice for the novelty to reveal memory for previously encountered objects, the OLT primarily evaluates spatial learning, which relies heavily on hippocampal activity. The NORT, in contrast, evaluates non-spatial learning of object identity, which relies on multiple brain regions. Both tasks require an open-field-testing arena, objects with equivalent intrinsic value to mice, appropriate environmental cues, and video recording equipment and the software. Commercially available systems, while convenient, can be costly. This manuscript details a simple, cost-effective method for building the arenas and setting up the equipment necessary to perform the OLT and NORT. Furthermore, the manuscript describes an efficient testing protocol that incorporates both OLT and NORT and provides typical methods for data acquisition and analysis, as well as representative results. Successful completion of these tests can provide valuable insight into the memory function of various mouse model systems and appraise the underlying neural regions that support these functions.

Here we provide a protocol which includes comprehensive instructions for the economical establishment of murine object location and novel object recognition behavioral testing, including the design, cost, and construction of required equipment as well as execution of behavioral testing, data collection, and analysis.

Yeni nesne tanıma ve nesnenin konumunu bir bütçe farelerde test davranış

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various ...

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has been described. The exercise-induced cTnT response distorts the diagnostic role of the cTnT assay. Although high-intensity interval exercise (HIE) is growing in popularity and concerns remain about its safety, available data related to cTnT release after HIE is limited, which hampers the use of HIE as a health intervention. Here, we present three representative HIE protocols [traditional HIE (repeated 4 min cycling at 90% V&#775;O2max interspersed with 3 min rest, 200 kJ/session); sprint interval exercise (SIE, repeated 1 min cycling at 120% V&#775;O2max interspersed with 1.5 min rest, 200 kJ/session); and repeated sprint exercise (RSE, 40 x 6 s all-out sprints interspersed with 9 s rest)] and one representative MCE protocol (continuous cycling exercise at an intensity of 60% V&#775;O2max, 200 kJ/session). Forty-seven sedentary, overweight young women were randomly assigned to one of four groups (HIE, SIE, RSE, and MCE). Six bouts of respective exercise were performed by every single group, with each being 48 h apart. Meanwhile, for four groups, the duration of the entire testing period was identical, being 10 days. Before and after the first and final exercise bouts, an assessment was conducted of cTnT. The current study provides a frame of reference giving a clear picture of how a specific exercise session affects the circulating cTnT concentration at the early stage of training. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise, especially for HIE.

Here, we present protocols of high-intensity interval and moderate-intensity continuous exercise to observe the response of circulating cardiac troponin T (cTnT) concentration to acute exercise over 10 days. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise.

Eğitimin Erken Evresinde Yüksek Yoğunluklu Aralıkegzersizve Orta Yoğunluklu Sürekli Egzersizin Kardiyak Troponin T Düzeyine Etkisi

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has ...

Optogenetic Manipulation of Neuronal Activity to Modulate Behavior in Freely Moving Mice

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of single neurons and region-specific transmitter release, up to whole neuronal circuitries in the central nervous system, and allows the direct measurement of behavioral outcomes. Neurons express optogenetic tools via an injection of viral vectors carrying the DNA of choice, such as Channelrhodopsin2 (ChR2). Light is brought into specific brain regions via chronic optical implants that terminate directly above the target region. After two weeks of recovery and proper tool-expression, mice can be repeatedly used for behavioral tests with optogenetic stimulation of the neurons of interest.
Optogenetic modulation has a high temporal and spatial resolution that can be accomplished with high cell specificity, compared to the commonly used methods such as chemical or electrical stimulation. The light does not harm neuronal tissue and can therefore be used for long-term experiments as well as for multiple behavioral experiments in one mouse. The possibilities of optogenetic tools are nearly unlimited and enable the activation or silencing of whole neurons, or even the manipulation of a specific receptor type by light.
The results of such behavioral experiments with integrated optogenetic stimulation directly visualizes changes in behavior caused by the manipulation. The behavior of the same animal without light stimulation as a baseline is a good control for induced changes. This allows a detailed overview of neuronal types or neurotransmitter systems involved in specific behaviors, such as anxiety. The plasticity of neuronal networks can also be investigated in great detail through long-term stimulation or behavioral observations after optical stimulation. Optogenetics will help to enlighten neuronal signaling in several kinds of neurological diseases.

With optogenetic manipulation of specific neuronal populations or brain regions, behavior can be modified with high temporal and spatial resolution in freely moving animals. By using different optogenetic tools in combination with chronically implanted optical fibers, a variety of neuronal modulations and behavioral testing can be performed.

SerbestÇe Hareket Eden Farelerde Davranışı Modüle Etmek Için Nöronal Aktivitenin Optogenetik Manipülasyonu

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of ...