A transfemoral prosthesis is required to assist amputees to perform the activity of daily living (ADL). The passive prosthesis has some drawbacks such as utilization of high metabolic energy. In contrast, the active prosthesis consumes less metabolic energy and offers better performance. However, the recent active prosthesis uses surface electromyography as its sensory system which has weak signals with microvolt-level intensity and requires a lot of computation to extract features. This paper focuses on recognizing different phases of sitting and standing of a transfemoral amputee using in-socket piezoelectric-based sensors. 15 piezoelectric film sensors were embedded in the inner socket wall adjacent to the most active regions of the agonist and antagonist knee extensor and flexor muscles, i. e. region with the highest level of muscle contractions of the quadriceps and hamstring. A male transfemoral amputee wore the instrumented socket and was instructed to perform several sitting and standing phases using an armless chair. Data was collected from the 15 embedded sensors and went through signal conditioning circuits. The overlapping analysis window technique was used to segment the data using different window lengths. Fifteen time-domain and frequency-domain features were extracted and new feature sets were obtained based on the feature performance. Eight of the common pattern recognition multiclass classifiers were evaluated and compared. Regression analysis was used to investigate the impact of the number of features and the window lengths on the classifiers’ accuracies, and Analysis of Variance (ANOVA) was used to test significant differences in the classifiers’ performances. The classification accuracy was calculated using k-fold cross-validation method, and 20% of the data set was held out for testing the optimal classifier. The results showed that the feature set (FS-5) consisting of the root mean square (RMS) and the number of peaks (NP) achieved the highest classification accuracy in five classifiers. Support vector machine (SVM) with cubic kernel proved to be the optimal classifier, and it achieved a classification accuracy of 98.33 % using the test data set. Obtaining high classification accuracy using only two time-domain features would significantly reduce the processing time of controlling a prosthesis and eliminate substantial delay. The proposed in-socket sensors used to detect sit-to-stand and stand-to-sit movements could be further integrated with an active knee joint actuation system to produce powered assistance during energy-demanding activities such as sit-to-stand and stair climbing. In future, the system could also be used to accurately predict the intended movement based on their residual limb’s muscle and mechanical behaviour as detected by the in-socket sensory system.

中文翻译：

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基于经股截肢者中的插座内压电传感器对站立和就座阶段进行分类。

需要经股股骨假体来协助截肢者执行日常生活活动（ADL）。被动假体具有一些缺点，例如利用高代谢能量。相反，活性假体消耗较少的代谢能并提供更好的性能。然而，最近的主动假体使用表面肌电图作为其感觉系统，其具有微伏级强度的微弱信号，并且需要大量计算来提取特征。本文着重于利用基于插座的压电传感器识别经股截肢者坐下和站立的不同阶段。15个压电薄膜传感器被嵌入到内孔壁内，与激动剂和拮抗剂膝部伸肌和屈肌的最活跃区域相邻。e。股四头肌和绳肌的肌肉收缩水平最高的区域。一位男性经股截肢者戴着器械插座，并被指示使用无臂椅子进行几个坐立阶段。数据是从15个嵌入式传感器收集的，并经过信号调节电路。重叠分析窗口技术用于使用不同的窗口长度分割数据。提取了15个时域和频域特征，并基于特征性能获得了新的特征集。对八个常见模式识别多类分类器进行了评估和比较。回归分析用于调查特征数量和窗口长度对分类器精度的影响，方差分析（ANOVA）用于测试分类器性能的显着差异。使用k折交叉验证方法计算分类准确性，并保留20％的数据集以测试最佳分类器。结果表明，由均方根（RMS）和峰数（NP）组成的特征集（FS-5）在五个分类器中实现了最高的分类精度。支持向量机（SVM）具有三次核，被证明是最佳的分类器，使用测试数据集实现了98.33％的分类精度。仅使用两个时域特征获得高分类精度将显着减少控制假体的处理时间并消除大量延迟。提议的用于检测坐姿和站姿运动的插座式传感器可以与主动膝关节致动系统进一步集成，以在诸如坐姿和楼梯等能量需求活动中提供动力辅助攀登。将来，该系统还可用于根据其残余肢体的肌肉和由座内感觉系统检测到的机械行为来准确预测预期的运动。