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ActTrust - 可穿戴光感睡眠体动记录仪

简介 睡眠体动监测腕表,支持体动,环境及皮肤温度,环境光等参数的长期睡眠监测分析工具
型号 ActTrust

ActTrust - 光感睡眠体动记录仪Forschung_und_Entwicklung

ActTrust Actigraph测量手腕处的特殊参数,以检查睡眠和时间生物学。该设备专为研究应用而设计,是可靠而直观的睡眠监测工具。


  • 活动和睡眠阶段

  • 环境温度

  • 皮肤温度

  • 光强度

  • 蓝色、红色和绿色光波长

  • 紫外线UVA/UVB(** ActTrust2)


ActTrust 体动记录仪支持心理障碍的评估研究,如ADHD或Depression。


  • 监测神经调节技术(rTMS,tDCS,神经反馈)和行为,药物和光疗的功效,

  • 评估监测睡眠障碍,如入睡失眠,睡眠维持问题,昼夜节律紊乱,慢性疲劳),

  • 评估导致睡眠障碍发展的因素,例如睡前蓝光的消耗。


ActTrust 设备的功能

  • 2 个温度传感器、用于红色、绿色、蓝色和红外光的传感器

  • 一个 3 轴 MEMS 加速度计

  • 测量间隔 1- 86.400 秒

  • 事件标记

  • 连续记录和存储数据长达三个月

  • 坚固、防溅的外壳,

  • 通过 USB 扩展坞传输数据

  • 与 Windows 和 Mac OS 兼容的软件


ActStudio - 有效评估



  • 详细分析重要的睡眠参数:入睡所需的时间(入睡潜伏期 - SOL),入睡后醒来(WASO),睡眠效率和觉醒次数。

  • 各种时间生物学函数和环境因素的图形表示

  • 评估和科学用途的自动报告

通过扩展坞 ActDock 建立与计算机的连接。

References to Our Methods - ActTrust / ActStudio

Algorithm For Sleep Estimation

Automatic sleep/wake identification from wrist activity

Abstract: The purpose of this study was to develop and validate automatic scoring methods to distinguish sleep from wakefulness based on wrist activity. Forty-one subjects (l8 normals and 23 with sleep or psychiatric disorders) wore a wrist actigraph during overnight polysomnography. In a randomly selected subsample of 20 subjects, candidate sleep/wake prediction algorithms were iteratively optimized against standard sleep/wake scores. The optimal algorithms obtained for various data collection epoch lengths were then prospectively tested on the remaining 21 subjects. The final algorithms correctly distinguished sleep from wakefulness approximately 88% of the time. Actigraphic sleep percentage and sleep latency estimates correlated 0.82 and 0.90, respectively, with corresponding parameters scored from the polysomnogram (p < 0.0001). Automatic scoring of wrist activity provides valuable information about sleep and wakefulness that could be useful in both clinical and research applications.


Circadian research in mothers and infants: how many days of actigraphy data are needed to fit cosinor parameters?

Abstract: To determine the number of days of actigraphy data required to portray circadian rhythm in mothers and their young infants.

Activity Detection Modes (PIM,ZCM E TAT)

Ambulatory monitoring of sleep disorders

Abstract: Background: Behavioural and functional activity monitoring has a long history in sleep research. The term “Actigraphy” refers to methods using computerized wristwatch-size devices (generally placed on the wrist, but also on the ankle or trunk) to record the movement it undergoes. Collected data are displayed on a computer and analyzed for change in rhythm parameters that in turn provide an estimate on wake-sleep parameters (such as total sleep time, percent of time spent asleep, total wake time, percent of time spent awake and the number of awakenings). Actigraphy provides a useful, cost-effective, non-invasive and portable method for assessing specific sleep disorders. The present review is an amalgam of current knowledge with proposed clinical application and for research of actigraph. Conclusion: Actigraphy cannot stand alone as a diagnostic tool for all clinical groups. Particularly so with those diagnosed with sleep disorders with significant motility or long catatonic periods of wakefulness during sleep.

Non-Parametric Variables

A fresh look at the use of nonparametric analysis in actimetry

Abstract: Actimetry has been used to estimate the sleep–wake cycle instead of the rest-activity rhythm. Although algorithms for assessing sleep from actimetry data exist, it is useful to analyze the rest-activity rhythm using nonparametric methods. This would then allow rest-activity rhythm stability, fragmentation and amplitude to be quantified. In addition, sleep and wakefulness efficiency can be quantified separately. These variables have been used in studies analyzing the effect of age, diseases and their respective treatments on human circadian rhythmicity. In this study, we carried out a comprehensive analysis of the main results from published articles and devised a functional model of interaction among the several components involved in generating the sleep–wake cycle. The nonparametric variables render it possible to infer the main characteristics of circadian rhythms, such as synchronization with a zeitgeber, and its amplitude and robustness.