Der EcgMove 3 ist ein psychophysiologisches ambulantes Messsystem zur Erfassung des EKG-Signals und der körperlichen Aktivität. Er ist mit einer Bluetooth Smart Schnittstelle ausgestattet und hat die Möglichkeit, die gemessenen Daten live zu analysieren. Optimiert für den Einsatz in wissenschaftlichen Studien für das interaktive ambulante Assessment.

Der Sensor zeichnet die Rohdaten des EKG-Signals, der 3-Achsen-Beschleunigung, des barometrischen Höhensensors und der Temperatur über einen Zeitraum von bis zu 2 Wochen auf.

Aus diesen Messparametern können mit der Analyse-Software DataAnalyzer Ausgabeparameter wie Herzfrequenz, Herzratenvariabilität, Schritte, Aktivitätsklassen, Energieumsatz berechnet (Excel) und aussagekräftige Berichte (PDF) erzeugt werden.

Der Sensor kann sowohl mit einem Brustgurt (erhältlich in den Größen S, M und L, andere Größen auf Anfrage lieferbar) als auch mit Klebeelektroden genutzt werden.




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Top-Features

  • Live Analyse der Daten auf dem Sensor
  • Bluetooth-Smart Schnittstelle
  • Kombination von EKG- und Aktivitätsmessung in einem System
  • Bequemer Brustgurt liefert perfekte Signalqualität bei Langzeitmessungen (bis 2 Wochen), keine störenden Elektrodenkabel
  • Exakte und validierte Energieumsatzbestimmung und Erkennung von Alltagsaktivitäten
  • Nachhaltige Daten durch offenes Rohdatenformat
  • Praktikable und einfache Handhabung in Studien
  • Java API für USB (Windows)
  • API: Beispielimplementierung für Bluetooth Smart (Android)

Anwendungen

  • Interaktives Ambulantes Assessment
  • Mobiles EKG-Langzeit-Monitoring
  • Mobiles Langzeit-Monitoring von Herzrate und Herzratenvariabilität
  • Untersuchung des vegetativen Nervensystems
  • Verhaltensmonitoring
  • Psycho-physiologisches Belastungs-Monitoring
  • Energieumsatzschätzung und Aktivitätserkennung
  • Anwendung in der Psychoneuroimmunologie
  • Affective Computing
  • Integration in komplexe Systeme

Dazu passende Produkte und Dienstleistungen

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Software
Dokumentation
Beispieldaten
Beispielberichte
Externe Tools

Technische Daten

Power supply

Lithium-Polymer-Battery

Supply voltage

3 V

Battery voltage

2,7 - 4,2 V

Number of charging cycles

300 with 1C/1C > 80%

Maximum recording capacity

At least 2 weeks, depending on firmware configuration

Battery run time (recording, Bluetooth off)

~ 3 days

Size of sensor

(W x H x D)

62,3mm x 38,6mm x 11,5mm

Weight of sensor

26 g

Internal sensors

ECG-Amplifier:

Resolution: 12bit, Input range CM=560mV, DM=+/-5mV, Gain 227,

3db bandwidth 1,6 to 33Hz

Output rate: 256Hz to 1024Hz

 

3D acceleration sensor:

Measurement range: +/- 8 g

Noise: 4 mg

Output rate: 64 Hz

 

Pressure sensor:

Measurement range: 300 - 1100 hPa

Noise: 0,03 hPa

Output rate: 1 Hz

Indicators

LED, 3-color

(operation and charging status)

Vibrating alert (Marker)

Interfaces

Micro-USB, Bluetooth Smart

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

 

Humidity:

0 to 75% RH relative humidity

 

Atmospheric pressure:

300 to 1100 hPa absolute

Literatur und Validierung

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  • Paranoid Delusions as an Adaptive Response to Social Evaluative Stress?.
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  • Effectiveness of a smartphone-based worry-reduction training for stress reduction: A randomized-controlled trial.
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  • Brute Force ECG Feature Extraction Applied on Discomfort Detection.
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  • Physiological and cognitive performance of exposure to biophilic indoor environment.
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  • Mobile Sensors for Multiparametric Monitoring in Epileptic Patients.
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  • Emotions and Emotion Regulation in Economic Decision Making.
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  • Design and Evaluation of Affective Serious Games for Emotion Regulation Training.
    Petar Jercic (2013).
  • Komfortgewinn für Passagiere auf Langstreckenflügen durch den Einsatz chronobiologisch angepasster LED-Kabinenbeleuchtung.
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  • A Biofeedback Game for Training Arousal Regulation during a Stressful Task: The Space Investor.
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  • Measuring emotional arousal for online applications: Evaluation of ultra-short term heart rate variability measures.
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    S. Hey (2012).
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  • User Study: Motivation and User Acceptance of Using Physiological Data to Support Individual Reflection..
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  • From Stress Awareness to Coping Strategies of Medical Staff: Supporting Reflection on Physiological Data.
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