How to Train Your Posture: Haptic Feedback Can be Used for Postural Adaptation of the Trunk During Upper-Limb Motor Training.

Poor trunk posture, especially during long periods of sitting, could lead to problems such as Low Back Pain (LBP) and Forward Head Posture (FHP). Typical solutions are based on visual or vibration-based feedback. However, these systems could lead to feedback being ignored by the user and phantom vibration syndrome, respectively. In this study, we propose using haptic feedback for postural adaptation. In this two-part study, twenty-four healthy participants (age 25.87 ± 2.17 years) adapted to three different postural targets in the anterior direction while performing a unimanual reaching task using a robotic device. Results suggest a strong adaptation to the desired postural targets. Mean anterior trunk bending after the intervention is significantly different compared to baseline measurements for all postural targets. Additional analysis of movement straightness and smoothness indicates an absence of any negative interference of posture-based feedback on the performance of reaching movement. Taken together, these results suggest that haptic feedback-based systems could be used for postural adaptation applications. Also, this type of postural adaptation system can be used during the rehabilitation of stroke patients to reduce trunk compensation in lieu of typical physical constraint-based methods.

Inverse Saxophone-A Device to Study the Role of Individual Finger Perturbations on Grasp Stability.

The efficient coordination of fingertip forces to maintain static equilibrium while grasping an object continues to intrigue scientists. While many studies have explored this coordination, most of these studies assumed that interactions of hands primarily occur with rigid inanimate objects. Instead, our daily interactions with living and nonliving entities involve many dynamic, compliant, or fragile bodies. This paper investigates the fingertip force coordination on a manipulandum that changes its shape while grasping it. We designed a five-finger perturbation system with linear actuators at positions corresponding to each finger that would protrude outward from the center of the handle or retract toward the center of the handle as programmed. The behavior of the perturbed fingers and the other fingers while grasping this device was studied. Based on previous experiments on expanding and contracting handles, we hypothesized that each finger would exhibit a comparable response to similar horizontal perturbations. However, the response of the little finger was significantly different from the other fingers. We speculate that the central nervous system demonstrates preferential recruitment of some fingers over others while performing a task.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals.

Background: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results: The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Let the force guide you: a performance-based adaptive algorithm for postural training using haptic feedback.

Motor learning is an essential component of human behavior. Many different factors can influence the process of motor learning, such as the amount of practice and type of feedback. Changes in task difficulty during training can also considerably impact motor learning. Typical motor learning studies include a sequential variation of task difficulty, i.e., easy to challenging, irrespective of user performance. However, many studies have reported the importance of performance-based task difficulty variation for effective motor learning and skill transfer. A performance-based adaptive algorithm for task difficulty variation based on the challenge-point framework is proposed in this study. The algorithm is described for postural adaptation during simultaneous upper-limb training. Ten healthy participants (28 ± 2.44 years) were recruited to validate the algorithm. Participants adapted to a postural target of 20° in the anterior direction from the initial upright posture while performing a unimanual reaching task using a robotic device. Results suggest a significant decrease in postural error after training. The algorithm successfully adapted the task difficulty based on the performance of the user. The proposed algorithm could be modified for different motor skills and can be further evaluated for different applications in order to maximize the potential benefits of rehabilitation sessions.

Haptic feedback system for postural adaptation during robotic rehabilitation of upper limb.

Although trunk compensation during stroke rehabilitation is widely studied, the proposed solutions primarily include a trunk constraint, which has several disadvantages. In this study, we have proposed a haptic feedback-based system for postural training during upper-limb motor rehabilitation. We have tested the proposed system on six healthy people in this preliminary study. Participants performed a simple 1-dimensional reaching task while their posture was being monitored. They received haptic feedback based on their trunk posture. Preliminary results revealed a significant decline in postural error (p<0.05) after the haptic-based training. The reduction in error was maintained even after haptic feedback was turned off. This study shows that haptic feedback could be a viable alternative to the traditional constraint-based methods for postural adaptation. Additional studies need to be conducted to further evaluate the influence of using such feedback strategies.

Datasets of fingertip forces while grasping a handle with unsteady thumb platform.

This article presents the fingertip forces and moments data of the individual fingers and thumb when the thumb was placed on an unsteady platform, when the mass of the handle was systematically increased and when the thumb normal force was restricted while grasping a handle. Further, this article also includes a dataset while the thumb makes vertical movements such as extension (or upward motion) and flexion movement (or downward motion) during the static holding of a handle. An instrumented five-finger prehension handle was designed with a vertical railing on the thumb side. A slider platform was placed over the railing to mount the thumb force sensor. Further, a laser displacement sensor was mounted on top of the handle towards the thumb side to record the displacement of the thumb platform. The dataset includes fingertip forces, orientation of the handle, and the displacement data of thumb platform. This data helps therapists assess the degree of thumb disability, the contribution of ulnar fingers in establishing static equilibrium of a handheld object.

Comparing algorithms for assessing upper limb use with inertial measurement units.

The various existing measures to quantify upper limb use from wrist-worn inertial measurement units can be grouped into three categories: 1) Thresholded activity counting, 2) Gross movement score and 3) machine learning. However, there is currently no direct comparison of all these measures on a single dataset. While machine learning is a promising approach to detecting upper limb use, there is currently no knowledge of the information used by machine learning measures and the data-related factors that influence their performance. The current study conducted a direct comparison of the 1) thresholded activity counting measures, 2) gross movement score,3) a hybrid activity counting and gross movement score measure (introduced in this study), and 4) machine learning measures for detecting upper-limb use, using previously collected data. Two additional analyses were also performed to understand the nature of the information used by machine learning measures and the influence of data on the performance of machine learning measures. The intra-subject random forest machine learning measure detected upper limb use more accurately than all other measures, confirming previous observations in the literature. Among the non-machine learning (or traditional) algorithms, the hybrid activity counting and gross movement score measure performed better than the other measures. Further analysis of the random forest measure revealed that this measure used information about the forearm's orientation and amount of movement to detect upper limb use. The performance of machine learning measures was influenced by the types of movements and the proportion of functional data in the training/testing datasets. The study outcomes show that machine learning measures perform better than traditional measures and shed some light on how these methods detect upper-limb use. However, in the absence of annotated data for training machine learning measures, the hybrid activity counting and gross movement score measure presents a reasonable alternative. We believe this paper presents a step towards understanding and optimizing measures for upper limb use assessment using wearable sensors.

Comparable behaviour of ring and little fingers due to an artificial reduction in thumb contribution to hold objects.

BACKGROUND: The human hand plays a crucial role in accomplishing activities of daily living. The contribution of each finger in the human hand is remarkably unique in establishing object stabilization. According to the mechanical advantage hypothesis, the little finger tends to exert a greater normal force than the ring finger during a supination moment production task to stabilize the object. Similarly, during pronation, the index finger produces more normal force when compared with the middle finger. Hence, the central nervous system employs the peripheral fingers for torque generation to establish the equilibrium as they have a mechanical advantage of longer moment arms for normal force. In our study, we tested whether the mechanical advantage hypothesis is supported in a task in which the contribution of thumb was artificially reduced. We also computed the safety margin of the individual fingers and thumb. METHODOLOGY: Fifteen participants used five-finger prismatic precision grip to hold a custom-built handle with a vertical railing on the thumb side. A slider platform was placed on the railing such that the thumb sensor could move either up or down. There were two experimental conditions. In the "Fixed" condition, the slider was mechanically fixed, and hence the thumb sensor could not move. In the "Free" condition, the slider platform on which the thumb sensor was placed could freely move. In both conditions, the instruction was to grasp and hold the handle (and the platform) in static equilibrium. We recorded tangential and normal forces of all the fingers. RESULTS: The distribution of fingertip forces and moments changed depending on whether the thumb platform was movable (or not). In the free condition, the drop in the tangential force of thumb was counteracted by an increase in the normal force of the ring and little finger. Critically, the normal forces of the ring and little finger were statistically equivalent. The safety margin of the index and middle finger did not show a significant drop in the free condition when compared to fixed condition. CONCLUSION: We conclude that our results does not support the mechanical advantage hypothesis at least for the specific mechanical task considered in our study. In the free condition, the normal force of little finger was comparable to the normal force of the ring finger. Also, the safety margin of the thumb and ring finger increased to prevent slipping of the thumb platform and to maintain the handle in static equilibrium during the free condition. However, the rise in the safety margin of the ring finger was not compensated by a drop in the safety margin of the index and middle finger.

Motor sequence learning data collected continuously for fifteen days of practice using a novel glove-based typing device.

The dataset presented in the article includes the timestamp of key press and key release data of individual participants during a novel finger thumb opposition typing task. The novel task involves touching different segments (phalanges) of fingers with the thumb to type a specific symbol on the computer screen. This task involves learning of set of sequences by typing or touching them using the finger thumb opposition movements is termed as motor sequence learning task or paradigm. The symbol set comprised of nine most frequently used symbols in English. From the nine symbols, a set of 281 meaningful five lettered words (sequences) were formed. These sequences were presented to the participants in a game-like interface. Once a specific symbol was pressed and released the time stamp was registered in the computer as key (symbol) press and key release information. The dataset consists of three columns, first column shows the pressed key, second column the registered timestamp and final column shows the symbol activity with respect to the first symbol in terms of milliseconds. Key press information is followed by key release information. This is represented in the dataset as "LCONTROL" in the first column of the data. Changes of this key press and key release information over the course of practice can be used to understand change in performance of this novel tying task.

Wrist Posture Does Not Influence Finger Interdependence.

A task involving an instructed finger movement causes involuntary movements in the noninstructed fingers of the hand, also known as finger interdependence. It is associated with both mechanical and neural mechanisms. The current experiment investigated the effect of finger interdependence due to systematic changes of the wrist posture, close to neutral. Eight right-handed healthy human participants performed submaximal cyclic flexion and extension at the metacarpophalangeal joint at 0° neutral, 30° extension, and 30° flexion wrist postures, respectively. The experiment comprised of an instruction to move one of the 4 fingers-index, middle, ring, and little. Movements of the instructed and noninstructed fingers were recorded. Finger interdependence was quantified using enslavement matrix, individuation index, and stationarity index, and it was compared across wrist postures. The authors found that the finger interdependence does not change with changes in wrist posture. Further analysis showed that individuation and stationarity indices were mostly equivalent across wrist postures, and their effects were much smaller than the average differences present among the fingers. The authors conclude that at wrist postures close to neutral, the finger interdependence is not affected by wrist posture.

Posture similarity index: a method to compare hand postures in synergy space.

BACKGROUND: The human hand can perform a range of manipulation tasks, from holding a pen to holding a hammer. The central nervous system (CNS) uses different strategies in different manipulation tasks based on task requirements. Attempts to compare postures of the hand have been made for use in robotics and animation industries. In this study, we developed an index called the posture similarity index to quantify the similarity between two human hand postures. METHODS: Twelve right-handed volunteers performed 70 postures, and lifted and held 30 objects (total of 100 different postures, each performed five times). A 16-sensor electromagnetic tracking system captured the kinematics of individual finger phalanges (segments). We modeled the hand as a 21-DoF system and computed the corresponding joint angles. We used principal component analysis to extract kinematic synergies from this 21-DoF data. We developed a posture similarity index (PSI), that represents the similarity between posture in the synergy (Principal component) space. First, we tested the performance of this index using a synthetic dataset. After confirming that it performs well with the synthetic dataset, we used it to analyze the experimental data. Further, we used PSI to identify postures that are "representative" in the sense that they have a greater overlap (in synergy space) with a large number of postures. RESULTS: Our results confirmed that PSI is a relatively accurate index of similarity in synergy space both with synthetic data and real experimental data. Also, more special postures than common postures were found among "representative" postures. CONCLUSION: We developed an index for comparing posture similarity in synergy space and demonstrated its utility by using synthetic dataset and experimental dataset. Besides, we found that "special" postures are actually "special" in the sense that there are more of them in the "representative" postures as identified by our posture similarity index.

Thumbs up: movements made by the thumb are smoother and larger than fingers in finger-thumb opposition tasks.

BACKGROUND: In humans, the thumb plays a crucial role in producing finger opposition movements. These movements form the basis of several activities of the hand. Hence these movements have been used to study phenomena like prehension, motor control, motor learning, etc. Although such tasks have been studied extensively, the relative contribution of the thumb vis-à-vis the fingers in finger opposition tasks is not well understood. In this study, we investigated the kinematics of thumb and fingers in a simple finger opposition task. Further, we quantified the relative contribution and the movement smoothness aspects and compared these between fingers and thumb. METHODS: Eight, young healthy participants (four males and four females) were asked to perform a full finger to thumb opposition movement, where they were required to reach for different phalanges of the fingers. Position (X, Y and Z) of individual segments of the four fingers and the thumb were measured with reference to the wrist by a 16-sensor kinematics measurement system. Displacements and velocities were computed. An index, displacement ratio, that quantifies the relative contribution of thumb and fingers was computed from displacement data. Velocity data was used to quantify the smoothness of movement of thumb and fingers. RESULTS: The Displacement Ratio showed that contribution of the thumb is higher than contribution of any other target finger or target phalanges, except for the distal phalanx of the index and middle fingers. Smoothness of movement of the thumb was higher than all the finger phalanges in all cases. CONCLUSION: We conclude that in the task considered (thumb opposition movements to different targets within the hand & fingers), the thumb made a greater relative contribution in terms of displacement ratio and also produced smoother movements. However, smoothness of thumb did not vary depending on the target. This suggests that the traditional notion of the thumb being a special digit when compared to other fingers is true at least for the opposition movements considered in this study.

The sources of two components of variance: an example of multifinger cyclic force production tasks at different frequencies.

In a multifinger cyclic force production task, the finger force variance measured across trials can be decomposed into two components, one that affects the combined force output ("bad variance") and one that does not ("good variance"). Previous studies have found similar time patterns of "bad variance" and force rate leading to an approximately linear relationship between them. Based on this finding and a recently developed model of multifinger force production, we expected the "bad variance" during cyclic force production to increase monotonically with the rate of force change, both within a cycle and across trials at different frequencies. Alternatively, "bad variance" could show a dependence on task frequency, not on actual force derivative values. Healthy subjects were required to produce cyclic force patterns to prescribed targets by pressing on unidimensional force sensors, at a frequency set by a metronome. The task was performed with only the index finger, and with all four fingers. In the task with all four fingers, the "good variance" increased approximately linearly with an increase in the force magnitude. The "bad variance" showed within-a-cycle modulation similar to that of the force rate. However, an increase in the frequency did not lead to an increase in the "bad variance" that could be expected based on the natural relationships between action frequency and the rate of force change modulation. The results have been interpreted in the framework of an earlier model of multifinger force production where "bad variance" is a result of variance of the timing parameter. The unexpected lack of modulation of the "bad variance" with frequency suggests a drop in variance of the timing parameter with increased frequency. This mechanism may serve to maintain a constant acceptable level of variance under different conditions.