Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly

Abstract

Due to a lack of transparency in both algorithm and validation methodology, it is difficult for researchers and clinicians to select the appropriate tracker for their application. The aim of this work is to transparently present an adjustable physical activity classification algorithm that discriminates between dynamic, standing, and sedentary behavior. By means of easily adjustable parameters, the algorithm performance can be optimized for applications using different target populations and locations for tracker wear. Concerning an elderly target population with a tracker worn on the upper leg, the algorithm is optimized and validated under simulated free-living conditions. The fixed activity protocol (FAP) is performed by 20 participants; the simulated free-living protocol (SFP) involves another 20. Data segmentation window size and amount of physical activity threshold are optimized. The sensor orientation threshold does not vary. The validation of the algorithm is performed on 10 participants who perform the FAP and on 10 participants who perform the SFP. Percentage error (PE) and absolute percentage error (APE) are used to assess the algorithm performance. Standing and sedentary behavior are classified within acceptable limits (±10% error) both under fixed and simulated free-living conditions. Dynamic behavior is within acceptable limits under fixed conditions but has some limitations under simulated free-living conditions. We propose that this approach should be adopted by developers of activity trackers to facilitate the activity tracker selection process for researchers and clinicians. Furthermore, we are convinced that the adjustable algorithm potentially could contribute to the fast realization of new applications.

Daily Life Benefits and Usage Characteristics of Dynamic Arm Supports in Subjects with Neuromuscular Disorders

“…DAS use and upper extremity motions were monitored using activity sensors, Figure 1 . The activity sensors (MOX, Maastricht Instruments, Maastricht, The Netherlands) were 3D accelerometers with inbuilt data loggers capable of at least seven days of recording with a sample rate of 25 Hz [ 32 , 33 ]. Sensors were placed similarly to previous work [ 24 ]; on the lateral side of the upper arm (UA), on the lower arm at the wrist (LA), and on the device’s base (DB), and in addition, on the supporting brace of the DAS in line with the wrist sensor (SB).…”

The Validation of a Pocket Worn Activity Tracker for Step Count and Physical Behavior in Older Adults during Simulated Activities of Daily Living

“…The first one is the activity classification algorithm presented and validated by Annegarn et al (2011) for healthy adults (MOX Annegarn ), where the adjustable classification algorithm originates from. The second one is the classification algorithm with application specific adjustable parameters itself (Bijnens et al, (2019). For application in an older adult target group wearing an activity tracker in their trouser pocket the optimized parameter settings are: a data segmentation window size of 2 s, an amount of physical activity threshold of five counts per second (cps) and an orientation threshold of 0.8 g. This application is referred to as Miss Activity, the parameter settings as MOX MissActivity .…”

“…Recently, an adjustable classification algorithm was published to optimize algorithm performance (Bijnens et al, 2019). Through easily adjustable algorithm parameters it is possible to optimize the performance of this algorithm for different target and tracker wear locations.…”

Smartphone App with an Accelerometer Enhances Patients’ Physical Activity Following Elective Orthopedic Surgery: A Pilot Study

“…Hospital Fit is designed to be used in hospitalized patients and consists of a smartphone application connected to an accelerometer. The algorithm of the accelerometer has been validated to differentiate lying and sitting from standing and walking in hospitalized patients [34][35][36]. It provides patients and physiotherapists feedback on the number of minutes spent standing and walking per day.…”