An official website of the United States government
Background: An induced loss of balance resulting from a postural perturbation has been reported as the primary source for postural instability leading to falls. Hence; early detection of postural instability with novel wearable sensor-based measures may aid in reducing falls and fall-related injuries. The purpose of the study was to validate the use of a stretchable soft robotic sensor (SRS) to detect ankle joint kinematics during both unexpected and expected slip and trip perturbations. Methods: Ten participants (age: 23.7 ± 3.13 years; height: 170.47 ± 8.21 cm; mass: 82.86 ± 23.4 kg) experienced a counterbalanced exposure of an unexpected slip, an unexpected trip, an expected slip, and an expected trip using treadmill perturbations. Ankle joint kinematics for dorsiflexion and plantarflexion were quantified using three-dimensional (3D) motion capture through changes in ankle joint range of motion and using the SRS through changes in capacitance when stretched due to ankle movements during the perturbations. Results: A greater R-squared and lower root mean square error in the linear regression model was observed in comparing ankle joint kinematics data from motion capture with stretch sensors. Conclusions: Results from the study demonstrated that 71.25% of the trials exhibited a minimal error of less than 4.0 degrees difference from the motion capture system and a greater than 0.60 R-squared value in the linear model; suggesting a moderate to high accuracy and minimal errors in comparing SRS to a motion capture system. Findings indicate that the stretch sensors could be a feasible option in detecting ankle joint kinematics during slips and trips.
more »
« less
Improved Continuum Joint Configuration Estimation Using a Linear Combination of Length Measurements and Optimization of Sensor Placement
This paper presents methods for placing length sensors on a soft continuum robot joint as well as a novel configuration estimation method that drastically minimizes configuration estimation error. The methods utilized for placing sensors along the length of the joint include a single joint length sensor, sensors lined end-to-end, sensors that overlap according to a heuristic, and sensors that are placed by an optimization that we describe in this paper. The methods of configuration estimation include directly relating sensor length to a segment of the joint's angle, using an equal weighting of overlapping sensors that cover a joint segment, and using a weighted linear combination of all sensors on the continuum joint. The weights for the linear combination method are determined using robust linear regression. Using a kinematic simulation we show that placing three or more overlapping sensors and estimating the configuration with a linear combination of sensors resulted in a median error of 0.026% of the max range of motion or less. This is over a 500 times improvement as compared to using a single sensor to estimate the joint configuration. This error was computed across 80 simulated robots of different lengths and ranges of motion. We also found that the fully optimized sensor placement performed only marginally better than the placement of sensors according to the heuristic. This suggests that the use of a linear combination of sensors, with weights found using linear regression is more important than the placement of the overlapping sensors. Further, using the heuristic significantly simplifies the application of these techniques when designing for hardware.
more »
« less
- Award ID(s):
- 1935312
- PAR ID:
- 10310964
- Date Published:
- Journal Name:
- Frontiers in Robotics and AI
- Volume:
- 8
- ISSN:
- 2296-9144
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This study sought to evaluate the accuracy of a PPG (photoplethysmography) sensor designed to measure human heart rates in monitoring the distal limb pulse of healthy adult horses. We hypothesized that the PPG sensor is sensitive to placement location and orientation, and that measurement accuracies depend on placement and orientation on the limb. To evaluate this hypothesis, a completely randomized block design with a factorial treatment structure was used. Horses were considered as the block. Limb type (right front, left front, right hind, and left hind) and position of sensor (medial or lateral) were treatments, with levels arranged in a complete (4x2) factorial design. Data were collected by placing the PPG sensor on the limb of each horse (n= 6), with placement location according to the treatment (limb type and location) combination, and taking pulse readings for 60 seconds. Manual heart rates were collected concurrently using a stethoscope. Data were analyzed by calculating root mean square errors (RMSE) for the PPG measurements with the manual heart rates as a gold standard. Variation in RMSE associated with limb and location of sensor were evaluated using a general linear model with fixed effects for limb and location and a random effect for horse. Our results indicated that the PPG sensor was ineffective at measuring horse heart rates, and that the device was insensitive to placement location and orientation. Future work should focus on developing alternative analytics to interpret the data from PPG sensors to better reflect horse heart rates.more » « less
-
Le, Khanh N.Q. (Ed.)In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.more » « less
-
Accurate estimation of localized occupancy related information in real time enables a broad range of intelligent smart environment applications. A large number of studies using heterogeneous sensor arrays reflect the myriad requirements of various emerging pervasive, ubiquitous and participatory sensing applications. In this paper, we introduce a zero-configuration and infrastructure-less smartphone based location specific occupancy estimation model. We opportunistically exploit smartphone’s acoustic sensors in a conversing environment and motion sensors in absence of any conversational data. We demonstrate a novel speaker estimation algorithm based on unsupervised clustering of overlapped and non-overlapped conversational data and a change point detection algorithm for locomotive motion of the users to infer the occupancy. We augment our occupancy detection model with a fingerprinting based methodology using smartphone’s magnetometer sensor to accurately assimilate location information of any gathering. We postulate a novel crowdsourcing-based approach to annotate the semantic location of the occupancy. We evaluate our algorithms in different contexts; conversational, silence and mixed in presence of 10 domestic users. Our experimental results on real-life data traces in natural settings show that using this hybrid approach, we can achieve approximately 0.76 error count distance for occupancy detection accuracy on average.more » « less
-
Given a linear dynamical system affected by noise, we consider the problem of optimally placing sensors (at design-time) subject to certain budget constraints to minimize the trace of the steady-state error covariance of the Kalman filter. Previous work has shown that this problem is NP-hard in general. In this paper, we impose additional structure by considering systems whose dynamics are given by a stochastic matrix corresponding to an underlying consensus network. In the case when there is a single input at one of the nodes in a tree network, we provide an optimal strategy (computed in polynomial-time) to place the sensors over the network. However, we show that when the network has multiple inputs, the optimal sensor placement problem becomes NP-hard.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/frobt.2021.637301
