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• W4382940048 abstract "Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson’s correlation coefficient (r &gt; 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM ( r position = 0.9099 and r moment = 0.9707) whereas, PL ( r position = 0.9001, r moment = 0.9703) and GMax+VM ( r position = 0.9010, r moment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking." @default.
• W4382940048 created "2023-07-04" @default.
• W4382940048 creator A5027505203 @default.
• W4382940048 creator A5077475147 @default.
• W4382940048 creator A5092386087 @default.
• W4382940048 date "2023-07-03" @default.
• W4382940048 modified "2023-09-27" @default.
• W4382940048 title "The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development" @default.
• W4382940048 cites W1567010277 @default.
• W4382940048 cites W1569990971 @default.
• W4382940048 cites W1689711448 @default.
• W4382940048 cites W1921426946 @default.
• W4382940048 cites W1979892952 @default.
• W4382940048 cites W1993657848 @default.
• W4382940048 cites W1998320749 @default.
• W4382940048 cites W2000439420 @default.
• W4382940048 cites W2002499457 @default.
• W4382940048 cites W2006749861 @default.
• W4382940048 cites W2013463953 @default.
• W4382940048 cites W2014734344 @default.
• W4382940048 cites W2015024657 @default.
• W4382940048 cites W2019675885 @default.
• W4382940048 cites W2031240554 @default.
• W4382940048 cites W2039548554 @default.
• W4382940048 cites W2059530986 @default.
• W4382940048 cites W2063016156 @default.
• W4382940048 cites W2072588720 @default.
• W4382940048 cites W2074603332 @default.
• W4382940048 cites W2087612673 @default.
• W4382940048 cites W2093185395 @default.
• W4382940048 cites W2094463511 @default.
• W4382940048 cites W2095375385 @default.
• W4382940048 cites W2097904002 @default.
• W4382940048 cites W2106133733 @default.
• W4382940048 cites W2106951868 @default.
• W4382940048 cites W2120348227 @default.
• W4382940048 cites W2123167643 @default.
• W4382940048 cites W2132728264 @default.
• W4382940048 cites W2141501778 @default.
• W4382940048 cites W2142114107 @default.
• W4382940048 cites W2144910137 @default.
• W4382940048 cites W2153199313 @default.
• W4382940048 cites W2156914236 @default.
• W4382940048 cites W2157576363 @default.
• W4382940048 cites W2158728671 @default.
• W4382940048 cites W2167221577 @default.
• W4382940048 cites W2171188488 @default.
• W4382940048 cites W2220291955 @default.
• W4382940048 cites W2309973640 @default.
• W4382940048 cites W2321409658 @default.
• W4382940048 cites W2330701174 @default.
• W4382940048 cites W2465568626 @default.
• W4382940048 cites W2590167340 @default.
• W4382940048 cites W2683490996 @default.
• W4382940048 cites W2753528279 @default.
• W4382940048 cites W2762367260 @default.
• W4382940048 cites W2764025497 @default.
• W4382940048 cites W2789801633 @default.
• W4382940048 cites W2791995750 @default.
• W4382940048 cites W2792994057 @default.
• W4382940048 cites W2796420764 @default.
• W4382940048 cites W2803381593 @default.
• W4382940048 cites W2805546547 @default.
• W4382940048 cites W2883939927 @default.
• W4382940048 cites W2885168080 @default.
• W4382940048 cites W2885493485 @default.
• W4382940048 cites W2896671577 @default.
• W4382940048 cites W2897676283 @default.
• W4382940048 cites W2910617149 @default.
• W4382940048 cites W2915722752 @default.
• W4382940048 cites W2942786135 @default.
• W4382940048 cites W2946103159 @default.
• W4382940048 cites W2982586672 @default.
• W4382940048 cites W2983598339 @default.
• W4382940048 cites W2991240488 @default.
• W4382940048 cites W2993384677 @default.
• W4382940048 cites W2997304337 @default.
• W4382940048 cites W3004575838 @default.
• W4382940048 cites W3016053201 @default.
• W4382940048 cites W3017470925 @default.
• W4382940048 cites W3020019805 @default.
• W4382940048 cites W3025193462 @default.
• W4382940048 cites W3032981784 @default.
• W4382940048 cites W3042774406 @default.
• W4382940048 cites W3049129853 @default.
• W4382940048 cites W3050594648 @default.
• W4382940048 cites W3082986793 @default.
• W4382940048 cites W3090200077 @default.
• W4382940048 cites W3093894898 @default.
• W4382940048 cites W3130294542 @default.
• W4382940048 cites W3141797743 @default.
• W4382940048 cites W3144653585 @default.
• W4382940048 cites W3158171019 @default.
• W4382940048 cites W3171781543 @default.
• W4382940048 cites W3181506109 @default.
• W4382940048 cites W3188994910 @default.
• W4382940048 cites W3194226508 @default.
• W4382940048 cites W3195102198 @default.
• W4382940048 cites W3196554643 @default.
• W4382940048 cites W3206939209 @default.

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