We present BIOGEM, a fully biodegradable McKibben actuator with integrated sensing, made from gelatin-based composites. By tailoring the material compositions, we customize the mechanical and electrical properties of the biodegradable composites, creating an integrated biodegradable system that combines both actuation and sensing functionalities. BIOGEM integrates a McKibben actuating structure by using stiff gelatin as outer braiding and the stretchable gelatin as air chambers. It also integrates resistive strain sensing through ionic gelatin, allowing the actuator to monitor its own deformation without relying on conventional electronics. We characterize the actuator’s performance across key parameters including braid angle, wall thickness, and material stiffness, demonstrating reliable contraction and repeatable force output at low pressures. Biodegradation is validated through both enzyme-assisted and backyard soil studies, confirming the material’s sustainable end-of-life behavior under realistic conditions. We illustrate the potential of this platform through interactive, edible, and environmentally-degradable prototypes across human–computer interaction and soft robotics scenarios.

I am currently assisting PhD student Jessica Luo in her research of the Sanmen Wu sound system, a language of the Wu family found in Southeast China. As Jessica writes an article that summarizes the sound structure of Sanmen Wu, I analyze utterances produced by speakers of the language. In my self-guided research, I focus on the sound quality of the consonants and their variations to determine underlying pronunciation. I also connect these variations to historical sound changes from Middle Chinese, its ancestor, into Sanmen Wu. I observe that Sanmen Wu speakers tend to freely alter pronunciations of certain consonants. For example, a speaker may say 部 [pu] or [bu] meaning ‘part,’ the latter only appearing after another spoken word. These two syllables contrast only in voicing, where [p] is voiceless and [b] is voiced. I use Praat, an industry-standard speech-analysis program, to read diagrams that depict the acoustics of these consonants to verify my findings. I am also creating a set of rules that predicts this alternation. One of the conditions is as follows: words with alternating voicing in their consonants change when pronounced within a sentence (‘medially’). Eventually, I will explain these rules, and I predict my explanation is related to the evolution of Sanmen Wu into its current stage. I reason that because the Wu language family stems from Middle Chinese, both of which require contrastive voicing to create distinct words, Sanmen Wu also contains the original underlying variation that exists in Middle Chinese. As such, I attribute this variation to an inherent part of the language rather than random circumstance. Ultimately, I intend to foster a thorough understanding of Sanmen Wu phonology and provide a foundation for further exploration of this topic.

Intrinsic hand muscles and tendons are crucial for joint stabilization, fine motor control, and coordinating flexion—functions essential for performing dexterous tasks such as typing, grasping, and tool manipulation. However, monitoring strength and real-time activities remains challenging. Surface electromyography (EMG) struggles to isolate signals from interior tissue due to low signal-to-noise ratios. Devices like the Rotterdam Intrinsic Hand Myometer measure strength but are cumbersome for continuous monitoring. Electrical Impedance sensing offers a promising alternative. This technique passes a low-frequency electrical current through electrode pairs (injectors and receivers) on the skin and measures the resulting voltage changes to model tissue impedance. Through this approach, we can track and classify the activity of hand muscles and tendons in real-time, targeting the challenge of capturing signals within the hand. Our approach integrates a custom conductive fabric electrode array into a wearable form, such as a glove or a flexible bandage, to detect impedance variation with muscle contractions. These signals are processed through a regression-based machine-learning algorithm that predicts hand poses. A dynamic simulation further visualizes the motion and corresponding muscle activity, providing feedback on intrinsic muscle coordination. By offering real-time monitoring of deeper musculoskeletal dynamics, our system opens new avenues for analyzing muscle function and optimizing performance. Beyond research, this system can inform a range of applications—from enhancing human-computer interaction and prosthetic control to supporting personalized rehabilitation protocols. Looking ahead, we plan to optimize electrode designs for improved comfort and precision and to incorporate advanced machine-learning techniques for enhanced pose prediction. Through refinements, we aim to make EIS-based hand muscle monitoring a versatile tool for researchers, clinicians, and innovators across diverse fields.

Physical rehabilitation sensing technologies play a critical role in enhancing patients’ recovery through real-time monitoring and help doctors evaluate the effectiveness of rehabilitation treatment. However, traditional sensing devices are bulky which may not only limit the patients’ movement and reduce the accuracy of doctors’ evaluation but also add additional burden to the patients. Thus, we want to develop a unified multimodal sensing wearable, capturing multimodal data in a more compact and efficient form factor, allowing the patients to perform rehab tasks in a more natural way. While a multimodal wearables strategy does exist, their sensors usually stack on top of each other for multimodality, which sacrifices flexibility, and compactness, making patients inconvenient to wear. To address these limitations, we developed a novel multifunctional smart oversleeve, integrating a customized portable sensing circuit that can perform joint deformation monitoring and measure muscle activities through electrical impedance tomography (EIT) and electromyography (EMG). The sensors of the circuits are highly unified as well as the readout circuit. Compared with traditional bulky sensors with multiple layers, patients can easily wear them as regular sleeves, which combines the function of reading all of the signals that are mentioned above (EMG, EIT, etc.). From the perspective of the circuit, it can effectively calculate the bending angles of the elbow and track the activities of the bicep, triceps, and forearm muscles, reflecting the patients’ recovery performance. These capabilities provide valuable insights into patient recovery performance and highlight the potential of this device as a versatile tool for physical rehabilitation monitoring.