Evaluating Real-World Ambulation and Activity in Prosthetic Users with Wearable Sensors

Author(s):  
Samuel Lyons ◽  
Joseph Smith ◽  
Ava Segal ◽  
Michael Orendurff
Keyword(s):  
Author(s):  
Jennifer Merickel ◽  
Robin High ◽  
Lynette Smith ◽  
Chris Wichman ◽  
Emily Frankel ◽  
...  

This pilot study tackles the overarching need for driver-state detection through real-world measurements of driver behavior and physiology in at-risk drivers with type 1 diabetes mellitus (DM). 35 drivers (19 DM, 14 comparison) participated. Real-time glucose levels were measured over four weeks with continuous glucose monitor (CGM) wearable sensors. Contemporaneous real-world driving performance and behavior were measured with in-vehicle video and electronic sensor instrumentation packages. Results showed clear links between at-risk glucose levels (particularly hypoglycemia) and changes in driver performance and behavior. DM participants often drove during at-risk glucose levels (low and high) and showed cognitive impairments in key domains for driving, which are likely linked to frequent hypoglycemia. The finding of increased driving risk in DM participants was mirrored in state records of crashes and traffic citations. Combining sensor data and phenotypes of driver behavior can inform patients, caregivers, safety interventions, policy, and design of supportive in-vehicle technology that is responsive to driver state.


2020 ◽  
Vol 7 ◽  
Author(s):  
Shirley Handelzalts ◽  
Neil B. Alexander ◽  
Nicholas Mastruserio ◽  
Linda V. Nyquist ◽  
Debra M. Strasburg ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jamie L. Adams ◽  
Karthik Dinesh ◽  
Christopher W. Snyder ◽  
Mulin Xiong ◽  
Christopher G. Tarolli ◽  
...  

AbstractMost wearable sensor studies in Parkinson’s disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson’s disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson’s walked significantly less (median [inter-quartile range]: 4980 [2835–7163] steps/day) than controls (7367 [5106–8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4–5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1–4) of individuals with Parkinson’s, which was significantly higher than the 0.5 [0.3–2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson’s in real-world settings.


Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2631
Author(s):  
Lucy Coates ◽  
Jian Shi ◽  
Lynn Rochester ◽  
Silvia Del Din ◽  
Annette Pantall

Parkinson’s disease (PD) is a common age-related neurodegenerative disease. Gait impairment is frequent in the later stages of PD contributing to reduced mobility and quality of life. Digital biomarkers such as gait velocity and step length are predictors of motor and cognitive decline in PD. Additional gait parameters may describe different aspects of gait and motor control in PD. Sample entropy (SampEnt), a measure of signal predictability, is a nonlinear approach that quantifies regularity of a signal. This study investigated SampEnt as a potential biomarker for PD and disease duration. Real-world gait data over a seven-day period were collected using an accelerometer (Axivity AX3, York, UK) placed on the low back and gait metrics extracted. SampEnt was determined for the stride time, with vector length and threshold parameters optimized. People with PD had higher stride time SampEnt compared to older adults, indicating reduced gait regularity. The range of SampEnt increased over 36 months for the PD group, although the mean value did not change. SampEnt was associated with dopaminergic medication dose but not with clinical motor scores. In conclusion, this pilot study indicates that SampEnt from real-world data may be a useful parameter reflecting clinical status although further research is needed involving larger populations.


2021 ◽  
Author(s):  
Johanna Happold ◽  
Robert Richer ◽  
Arne Kuderle ◽  
Heiko Gabner ◽  
Jochen Klucken ◽  
...  
Keyword(s):  

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2948
Author(s):  
Takayuki Nozawa ◽  
Mizuki Uchiyama ◽  
Keigo Honda ◽  
Tamio Nakano ◽  
Yoshihiro Miyake

Speech discrimination that determines whether a participant is speaking at a given moment is essential in investigating human verbal communication. Specifically, in dynamic real-world situations where multiple people participate in, and form, groups in the same space, simultaneous speakers render speech discrimination that is solely based on audio sensing difficult. In this study, we focused on physical activity during speech, and hypothesized that combining audio and physical motion data acquired by wearable sensors can improve speech discrimination. Thus, utterance and physical activity data of students in a university participatory class were recorded, using smartphones worn around their neck. First, we tested the temporal relationship between manually identified utterances and physical motions and confirmed that physical activities in wide-frequency ranges co-occurred with utterances. Second, we trained and tested classifiers for each participant and found a higher performance with the audio-motion classifier (average accuracy 92.2%) than both the audio-only (80.4%) and motion-only (87.8%) classifiers. Finally, we tested inter-individual classification and obtained a higher performance with the audio-motion combined classifier (83.2%) than the audio-only (67.7%) and motion-only (71.9%) classifiers. These results show that audio-motion multimodal sensing using widely available smartphones can provide effective utterance discrimination in dynamic group communications.


Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3805 ◽  
Author(s):  
Kalliopi Kyriakou ◽  
Bernd Resch ◽  
Günther Sagl ◽  
Andreas Petutschnig ◽  
Christian Werner ◽  
...  

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.


2015 ◽  
Vol 17 (1) ◽  
pp. 16-33 ◽  
Author(s):  
Kathryn S. Hayward ◽  
Janice J. Eng ◽  
Lara A. Boyd ◽  
Bimal Lakhani ◽  
Julie Bernhardt ◽  
...  

The ultimate goal of upper-limb rehabilitation after stroke is to promote real-world use, that is, use of the paretic upper-limb in everyday activities outside the clinic or laboratory. Although real-world use can be collected through self-report questionnaires, an objective indicator is preferred. Accelerometers are a promising tool. The current paper aims to explore the feasibility of accelerometers to measure upper-limb use after stroke and discuss the translation of this measurement tool into clinical practice. Accelerometers are non-invasive, wearable sensors that measure movement in arbitrary units called activity counts. Research to date indicates that activity counts are a reliable and valid index of upper-limb use. While most accelerometers are unable to distinguish between the type and quality of movements performed, recent advancements have used accelerometry data to produce clinically meaningful information for clinicians, patients, family and care givers. Despite this, widespread uptake in research and clinical environments remains limited. If uptake was enhanced, we could build a deeper understanding of how people with stroke use their arm in real-world environments. In order to facilitate greater uptake, however, there is a need for greater consistency in protocol development, accelerometer application and data interpretation.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1502
Author(s):  
Diogo Schwerz de Lucena ◽  
Justin Rowe ◽  
Vicky Chan ◽  
David J. Reinkensmeyer

There are few wearable sensors suitable for daily monitoring of wrist and finger movements for hand-related healthcare applications. Here, we describe the development and validation of a novel algorithm for magnetically counting hand movements. We implemented the algorithm on a wristband that senses magnetic field changes produced by movement of a magnetic ring worn on the finger (the “Manumeter”). The “HAND” (Hand Activity estimated by Nonlinear Detection) algorithm assigns a “HAND count” by thresholding the real-time change in magnetic field created by wrist and/or finger movement. We optimized thresholds to achieve a HAND count accuracy of ~85% without requiring subject-specific calibration. Then, we validated the algorithm in a dexterity-impaired population by showing that HAND counts strongly correlate with clinical assessments of upper extremity (UE) function after stroke. Finally, we used HAND counts to test a recent hypothesis in stroke rehabilitation that real-world UE hand use increases only for stroke survivors who achieve a threshold level of UE functional capability. For 29 stroke survivors, HAND counts measured at home did not increase until the participants’ Box and Blocks Test scores exceeded ~50% normal. These results show that a threshold-based magnetometry approach can non-obtrusively quantify hand movements without calibration and also verify a key concept of real-world hand use after stroke.


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