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hmu

asmeragliuolo@gmail.com

a paper:

Validation of the Leap Motion Controller using markered motion capture technology
or
how well a Leap Motion can see your hand
by Anna Smeragliuolo, N.Jeremy Hill, Luis Disla, and David Putrino

read it here
Fig. 1. Upper limb anatomical landmarks for marker placement. 1, 2) the acromion processes; 3) center of the sternum; 4) lateral, and 5) medial humeral epicondyles; 6) ulnar, and 7) radial styloid processes; 8) the dorsal side of the wrist centered between the styloid processes; 9) center of the third metacarpal (back of the hand); 10) the middle of the first metacarpal; 11) the distal phalange of the thumb; 12) the distal phalange of the third (middle) finger. Fig. 2. (Leap Motion Inc.). Typical use: a user controls virtual hands with the Leap Motion. Fig. 4. Hand movements: a) radial/ulnar deviation, b) flexion/extension, and c) supination/pronation. Hand positions: d) open hand, e) loose fist, and f) tight fist. Fig. 5. Neutral positions of the wrist and corresponding axes. a) ISB recommended neutral wrist position, with grey arrows describing our rotational adjustments. b) Adjusted neutral wrist position for Leap Motion. Fig. 6. a) Flexion/extension Bias across range. Negative x-values represent flexion for both right and left hands; positive x-values represent extension for both hands. Mean bias 72 standard deviations are delineated on the plot to highlight differing accuracy levels. b) Flexion/extension RMSE across range: Negative x-values represent flexion for both right and left hands; positive x-values represent extension for both hands. Data points attributed to the right hand are represented by warm colors, the left hand by cool colors. Effective ranges are shaded. The hands represented on the negative and positive sides of the x-axis indicate the directions of movement for both the right and left hands. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 7. a) Radial/ulnar deviation Bias magnitude across range: Negative x-values represent radial deviation of the right hand and ulnar deviation of the left hand; positive x- values represent ulnar deviation of the right hand and radial deviation of the left. Mean Bias 72 standard deviations are delineated on the plot to highlight differing accuracy levels. b) Radial/ulnar deviation RMSE across range: Negative x-values represent radial deviation of the right hand and ulnar deviation of the left hand; positive x- values represent ulnar deviation of the right hand and radial deviation of the left. Data points attributed to the right hand are represented by warm colors, the left hand by cool colors. Effective ranges are shaded. The hands represented on the negative and positive sides of the x-axis indicate the directions of movement for both the right and left hands. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 8. a) Pronation/supination Bias magnitude across range: Negative x-values represent supination of the right hand and pronation of the left hand; positive x-values represent pronation of the right hand and supination of the left. Mean Bias 72 standard deviations are delineated on the plot to highlight differing accuracy levels. b) Supination/pronation RMSE across range: Negative x-values represent supination of the right hand and pronation of the left hand; positive x-values represent pronation of the right hand and supination of the left. Data points attributed to the right hand are represented by warm colors, the left hand by cool colors. No effective range was identified for this movement. The hands represented on the negative and positive sides of the x-axis indicate the directions of movement for both the right and left hands. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

another paper:

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial
or
machine learning is how a Kinect can tell what you are doing better than a fancy force plate can
by Behdad Dehbandi, Alexandre Barachant, Anna Smeragliuolo, John Davis Long, Silverio Joseph Bumanlag, Victor He, Anna Lampe, and David Putrino

read it here
Fig 1. Experimental layout for data collection: subjects sat on a rectangular box that was placed in the center of the KFP at a distance of 2.7 meters from the MK2. The MK2 was mounted on a tripod 0.92 meters from the floor. Fig 2. Three postural conditions. a) Both feet on the ground. b) One foot off the ground. c) Both feet off the ground. Fig 6. t-SNE representation of the dataset for the MK2 (left) and the KFP (right). A clear separation between tasks can be observed for both devices, but the MK2 data are more separable. Each cluster is formed by the points of the 3 conditions performed under the corresponding task. A zoom over the cluster of task 5 highlights a lower separation of the conditions.

a poster:

Validation of the Leap Motion’s Hand Recognition Capabilities and Limitations poster presentation for Burke Medical Research Institute summer scholars poster day
by Anna Smeragliuolo, Divija Chopra, Matthew Marano, Luis Disla, and David Putrino