Upper limb activity of twenty myoelectric prosthesis users and twenty healthy anatomically intact adults

Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning

Trials for therapies after an upper limb amputation (ULA) require a focus on the real-world use of the upper limb prosthesis. In this paper, we extend a novel method for identifying upper extremity functional and nonfunctional use to a new patient population: upper limb amputees. We videotaped five amputees and 10 controls performing a series of minimally structured activities while wearing sensors on both wrists that measured linear acceleration and angular velocity. The video data was annotated to provide ground truth for annotating the sensor data. Two different analysis methods were used: one that used fixed-size data chunks to create features to train a Random Forest classifier and one that used variable-size data chunks. For the amputees, the fixed-size data chunk method yielded good results, with 82.7% median accuracy (range of 79.3-85.8) on the 10-fold cross-validation intra-subject test and 69.8% in the leave-one-out inter-subject test (range of 61.4-72.8). The variable-size data method did not improve classifier accuracy compared to the fixed-size method. Our method shows promise for inexpensive and objective quantification of functional upper extremity (UE) use in amputees and furthers the case for use of this method in assessing the impact of UE rehabilitative treatments.

Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks

Intent sensing—the ability to sense what a user wants to happen—has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.

Real-World Testing of the Self Grasping Hand, a Novel Adjustable Passive Prosthesis: A Single Group Pilot Study

(1) Background: This study investigated the feasibility of conducting a two-week “real-world” trial of the Self Grasping Hand (SGH), a novel 3D printed passive adjustable prosthesis for hand absence; (2) Methods: Single-group pilot study of nine adults with trans-radial limb absence; five used body-powered split-hooks, and four had passive cosmetic hands as their usual prosthesis. Data from activity monitors were used to measure wear time and bilateral activity. At the end of the two-week trial, function and satisfaction were measured using the Orthotics and Prosthetics Users’ Survey Function Scale (OPUS) and the prosthesis satisfaction sub-scales of the Trinity Amputations and Prosthesis Experience Scale (TAPES). Semi-structured interviews captured consumer feedback and suggestions for improvement; (3) Results: Average SGH wear time over 2 weeks was 17.5 h (10% of total prosthesis wear time) for split-hook users and 83.5 h (63% of total prosthesis wear time) for cosmetic hand users. Mean satisfaction was 5.2/10, and mean function score was 47.9/100; (4) Two-week real-world consumer testing of the SGH is feasible using the methods described. Future SGH designs need to be more robust with easier grasp lock/unlock.

Greater and More Natural Use of the Upper Limbs During Everyday Life by Former Amputees Versus Prosthesis Users

Hand loss profoundly impacts daily functioning. Reversal of amputation through hand replantation or transplantation offers an alternative to prosthetics for some. Whether recipients exhibit more extensive and natural limb use during everyday life than prosthesis users is, however, unknown.We asked unilateral, below-elbow amputees (N = 22), hand graft recipients (transplants N = 4; replants N = 2), and healthy matched controls (N = 20) to wear wireless accelerometers distally on their forearms/prostheses and proximally on their upper arms. These units captured limb activity over 3 days within participants' natural environments.Graft recipients exhibited heavier reliance on their affected hands compared to amputees' reliance on their prostheses, P < .001. Likewise, reliance on the injured side upper arm was also greater for hand graft recipients than amputees, regardless of whether they were wearing their prostheses, P < .05 in both cases. Hand graft recipients, like healthy controls, also relied more on forearm vs upper arm movements when controlling their limbs, P < .001.Compared with conventional prosthesis users, graft recipients exhibited more extensive and natural functioning of the upper limbs during everyday activities. This information is an important addition to other considerations when evaluating risk-benefit of these treatment alternatives.

Quantification of Upper Limb Isometric Force Control Abilities for Evaluating Upper Limb Functions Among Prosthetic Users

Force control abilities are essential to interact with objects in our environments. However, there is a lack of evaluation tools and methods to test the force control abilities of the upper limb in evaluating the upper limb functions of prosthetic users. This study aimed to quantify upper limb isometric force control abilities in healthy individuals and prosthetic users using a custom-built handle with a 6-axis force/torque sensor and visual cue, namely an Upper Limb End-effector type Force control test device (ULEF). Feasibilities of the test device were demonstrated through experiments by holding the ULEF with an intact hand among healthy subjects and transradial and wrist amputees with a myoelectric powered prosthetic hand, the bebionic hand. Compared to the healthy individuals, the prosthetic user group demonstrated poor isometric force control abilities in terms of higher control instability during the lateral direction task ( [Formula: see text]). Significantly higher variability in force-generating rates was also found in all task directions in the prosthetic user group ( [Formula: see text]). Compared to the healthy group, the prosthetic user group showed significant small peak biceps activities during the posterior task ( [Formula: see text]) and anterior task ( [Formula: see text]). Quantification of isometric upper limb force control abilities can potentially be beneficial to develop evaluation and research tools for investigating mechanisms underlying force control abilities of prosthetic users and provide guidelines for targeted isometric force control training and prosthesis development.

Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses

The efferent control chain for an upper-limb myoelectric prosthesis can be separated into 3 key areas: signal generation, signal acquisition, and device response. Data were collected from twenty trans-radial myoelectric prosthesis users using their own clinically prescribed devices, to establish the relative impact of these potential control factors on user performance (user functionality and everyday prosthesis usage). By identifying the key factor(s), we can guide future developments to ensure clinical impact. Skill in generating muscle signals was assessed via reaction times and signal tracking. To assess the predictability of signal acquisition, we inspected reaction time spread and undesired hand activations. As a measure of device response, we recorded the electromechanical delay between electrode stimulation and the onset of hand movement. Results suggest abstract measures of skill in controlling muscle signals are poorly correlated with performance. Undesired activations of the hand or incorrect responses were correlated with almost all kinematics and gaze measures suggesting unpredictability is a key factor. Significant correlations were also found between several measures of performance and the electromechanical delay; however, unexpectedly, longer electromechanical delays correlated with better performance. Future research should focus on exploring causes of unpredictability, their relative impacts on performance and interventions to address this.

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

Neuromuscular disorders cause progressive muscular weakness, which limits upper extremity mobility and performance during activities of daily life. Dynamic arm supports can improve mobility and quality of life. However, their use is often discontinued over time for unclear reasons. This study aimed to evaluate whether users of dynamic arm supports demonstrate and perceive quantifiable mobility benefits over a period of two months. Nine users of dynamic arm supports were included in this observational study. They had different neuromuscular disorders and collectively used four different arm supports. They were observed for three consecutive weeks during which they were equipped with a multi-sensor network of accelerometers to assess the actual use of the arm support and they were asked to provide self-reports on the perceived benefits of the devices. Benefits were experienced mainly during anti-gravity activities and the measured use did not change over time. The self-reports provided contextual information in domains such as participation to social life, in addition to the sensor system. However self-reports overestimated the actual use by up to three-fold compared to the accelerometer measures. A combination of objective and subjective methods is recommended for meaningful and quantifiable mobility benefits during activities of daily life.

Technology for monitoring everyday prosthesis use: a systematic review

BACKGROUND

Understanding how prostheses are used in everyday life is central to the design, provision and evaluation of prosthetic devices and associated services. This paper reviews the scientific literature on methodologies and technologies that have been used to assess the daily use of both upper- and lower-limb prostheses. It discusses the types of studies that have been undertaken, the technologies used to monitor physical activity, the benefits of monitoring daily living and the barriers to long-term monitoring, with particular focus on low-resource settings.

METHODS

A systematic literature search was conducted in PubMed, Web of Science, Scopus, CINAHL and EMBASE of studies that monitored the activity of prosthesis users during daily-living.

RESULTS

Sixty lower-limb studies and 9 upper-limb studies were identified for inclusion in the review. The first studies in the lower-limb field date from the 1990s and the number has increased steadily since the early 2000s. In contrast, the studies in the upper-limb field have only begun to emerge over the past few years. The early lower-limb studies focused on the development or validation of actimeters, algorithms and/or scores for activity classification. However, most of the recent lower-limb studies used activity monitoring to compare prosthetic components. The lower-limb studies mainly used step-counts as their only measure of activity, focusing on the amount of activity, not the type and quality of movements. In comparison, the small number of upper-limb studies were fairly evenly spread between development of algorithms, comparison of everyday activity to clinical scores, and comparison of different prosthesis user populations. Most upper-limb papers reported the degree of symmetry in activity levels between the arm with the prosthesis and the intact arm.

CONCLUSIONS

Activity monitoring technology used in conjunction with clinical scores and user feedback, offers significant insights into how prostheses are used and whether they meet the user's requirements. However, the cost, limited battery-life and lack of availability in many countries mean that using sensors to understand the daily use of prostheses and the types of activity being performed has not yet become a feasible standard clinical practice. This review provides recommendations for the research and clinical communities to advance this area for the benefit of prosthesis users.